{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:02:29Z","timestamp":1793318549535,"version":"4.5.0"},"reference-count":234,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Access Publication Fund of the University of Bonn"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB\/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB\/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB\/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB\/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.<\/jats:p>","DOI":"10.3390\/jcm12216833","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T13:20:07Z","timestamp":1698672007000},"page":"6833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0327-0890","authenticated-orcid":false,"given":"Elena","family":"Jost","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2201-1494","authenticated-orcid":false,"given":"Philipp","family":"Kosian","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1892-790X","authenticated-orcid":false,"given":"Jorge","family":"Jimenez Cruz","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-2211","authenticated-orcid":false,"given":"Shadi","family":"Albarqouni","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany"},{"name":"Helmholtz AI, Helmholtz Munich, Ingolst\u00e4dter Landstra\u00dfe 1, 85764 Neuherberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8284-4669","authenticated-orcid":false,"given":"Ulrich","family":"Gembruch","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3625-7097","authenticated-orcid":false,"given":"Brigitte","family":"Strizek","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9135-4338","authenticated-orcid":false,"given":"Florian","family":"Recker","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109717","DOI":"10.1016\/j.ejrad.2021.109717","article-title":"Artificial intelligence in ultrasound","volume":"139","author":"Shen","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_2","unstructured":"U.S. Food and Drug Administration (2023, August 21). Artificial Intelligence and Machine Learning (AI\/ML)-Enabled Medical Devices, Available online: https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1002\/uog.22122","article-title":"Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology","volume":"56","author":"Drukker","year":"2020","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"73","DOI":"10.33590\/emjreprohealth\/20-00100","article-title":"Deep Learning Strategies for Ultrasound in Pregnancy","volume":"6","author":"Diniz","year":"2020","journal-title":"EMJ Reprod. Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"151588","DOI":"10.1016\/j.semperi.2022.151588","article-title":"Artificial intelligence in perinatal diagnosis and management of congenital heart disease","volume":"46","author":"Reddy","year":"2022","journal-title":"Semin. Perinatol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1002\/uog.21967","article-title":"Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal","volume":"56","author":"Xie","year":"2020","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sakai, A., Komatsu, M., Komatsu, R., Matsuoka, R., Yasutomi, S., Dozen, A., Shozu, K., Arakaki, T., Machino, H., and Asada, K. (2022). Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening. Biomedicines, 10.","DOI":"10.3390\/biomedicines10030551"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100792","DOI":"10.1016\/j.ajogmf.2022.100792","article-title":"Use of artificial intelligence in obstetrics: Not quite ready for prime time","volume":"5","author":"Sarno","year":"2023","journal-title":"Am. J. Obstet. Gynecol. MFM"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Leung, K.-Y. (2021). Applications of Advanced Ultrasound Technology in Obstetrics. Diagnostics, 11, Available online: https:\/\/pubmed.ncbi.nlm.nih.gov\/34359300\/.","DOI":"10.3390\/diagnostics11071217"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.3109\/14767058.2015.1081891","article-title":"The feasibility of using 5D CNS software in obtaining standard fetal head measurements from volumes acquired by three-dimensional ultrasonography: Comparison with two-dimensional ultrasound","volume":"29","author":"Rizzo","year":"2016","journal-title":"J. Matern.-Fetal Neonatal Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1007\/s11548-015-1277-z","article-title":"Five-dimensional ultrasound system for soft tissue visualization","volume":"10","author":"Deshmukh","year":"2015","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"29","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"b2700","DOI":"10.1136\/bmj.b2700","article-title":"The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Healthcare Interventions: Explanation and Elaboration","volume":"339","author":"Liberati","year":"2009","journal-title":"BMJ"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/0002-9378(85)90298-4","article-title":"Estimation of fetal weight with the use of head, body, and femur measurements\u2014A prospective study","volume":"151","author":"Hadlock","year":"1985","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/TMI.2017.2712367","article-title":"SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound","volume":"36","author":"Baumgartner","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1002\/uog.10082","article-title":"Intra- and interobserver variability in fetal ultrasound measurements","volume":"39","author":"Sarris","year":"2012","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.1038\/s41598-023-29490-3","article-title":"Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries","volume":"13","author":"Campello","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5081","DOI":"10.1002\/mp.15700","article-title":"Efficient fetal ultrasound image segmentation for automatic head circumference measurement using a lightweight deep convolutional neural network","volume":"49","author":"Zeng","year":"2022","journal-title":"Med. Phys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1097\/AOG.0b013e3181a1d2f5","article-title":"Effect of Maternal Obesity on the Ultrasound Detection of Anomalous Fetuses","volume":"113","author":"Dashe","year":"2009","journal-title":"Obstet. Gynecol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1002\/jcu.23236","article-title":"The birth weight of macrosomia influence the accuracy of ultrasound estimation of fetal weight at term","volume":"50","author":"Song","year":"2022","journal-title":"J. Clin. Ultrasound"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1109\/JBHI.2017.2776116","article-title":"Automatic Estimation of Fetal Abdominal Circumference from Ultrasound Images","volume":"22","author":"Jang","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.diii.2018.08.001","article-title":"Artificial intelligence assistance for fetal head biometry: Assessment of automated measurement software","volume":"99","author":"Grandjean","year":"2018","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1002\/uog.22171","article-title":"Accuracy of automated three-dimensional ultrasound imaging technique for fetal head biometry","volume":"57","author":"Pluym","year":"2021","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.1109\/TMI.2021.3069663","article-title":"Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound","volume":"40","author":"Yang","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"P\u0142otka, S., Klasa, A., Lisowska, A., Seliga-Siwecka, J., Lipa, M., Trzci\u0144ski, T., and Sitek, A. (2022). Deep learning fetal ultrasound video model match human observers in biometric measurements. Phys. Med. Biol., 67.","DOI":"10.1088\/1361-6560\/ac4d85"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1016\/j.ultrasmedbio.2018.11.016","article-title":"Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks","volume":"45","author":"Sridar","year":"2019","journal-title":"Ultrasound Med. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10200","DOI":"10.1038\/s41598-020-67076-5","article-title":"Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes","volume":"10","author":"Eixarch","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1109\/TCYB.2017.2685080","article-title":"Ultrasound Standard Plane Detection Using a Composite Neural Network Framework","volume":"47","author":"Chen","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e24427","DOI":"10.1097\/MD.0000000000024427","article-title":"Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning","volume":"100","author":"Zhang","year":"2021","journal-title":"Medicine"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107017","DOI":"10.1016\/j.ultras.2023.107017","article-title":"Demystifying evidential Dempster Shafer-based CNN architecture for fetal plane detection from 2D ultrasound images leveraging fuzzy-contrast enhancement and explainable AI","volume":"132","author":"Rahman","year":"2023","journal-title":"Ultrasonics"},{"key":"ref_31","first-page":"571","article-title":"Automatic Fetal Measurements in Ultrasound Using Constrained Probabilistic Boosting Tree","volume":"Volume 4792","author":"Ayache","year":"2007","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2007, Proceedings of the 10th International Conference, Brisbane, Australia, 29 October\u20132 November 2007"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/TMI.2008.928917","article-title":"Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree","volume":"27","author":"Carneiro","year":"2008","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2258","DOI":"10.1016\/j.ultrasmedbio.2021.04.020","article-title":"A Prenatal Ultrasound Scanning Approach: One-Touch Technique in Second and Third Trimesters","volume":"47","author":"Luo","year":"2021","journal-title":"Ultrasound Med. Biol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1007\/s10278-020-00410-5","article-title":"Fetal Ultrasound Image Segmentation for Automatic Head Circumference Biometry Using Deeply Supervised Attention-Gated V-Net","volume":"34","author":"Zeng","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.ultrasmedbio.2018.09.015","article-title":"Automated Fetal Head Detection and Circumference Estimation from Free-Hand Ultrasound Sweeps Using Deep Learning in Resource-Limited Countries","volume":"45","author":"Heuvel","year":"2019","journal-title":"Ultrasound Med. Biol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/JBHI.2017.2703890","article-title":"Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting","volume":"22","author":"Li","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, C., Yang, Z., Liao, S., Guo, J., Yin, S., Liu, C., and Kang, Y. (2022). A new approach to automatic measure fetal head circumference in ultrasound images using convolutional neural networks. Comput. Biol. Med., 147.","DOI":"10.1016\/j.compbiomed.2022.105801"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, J., Petitjean, C., and Ainouz, S. (2022). Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images. J. Imaging, 8.","DOI":"10.3390\/jimaging8020023"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Arroyo, J., Marini, T.J., Saavedra, A.C., Toscano, M., Baran, T.M., Drennan, K., Dozier, A., Zhao, Y.T., Egoavil, M., and Tamayo, L. (2022). No sonographer, no radiologist: New system for automatic prenatal detection of fetal biometry, fetal presentation, and placental location. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262107"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2879","DOI":"10.1007\/s11517-020-02242-5","article-title":"Automated measurement network for accurate segmentation and parameter modification in fetal head ultrasound images","volume":"58","author":"Li","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105007","DOI":"10.1088\/1361-6579\/aae255","article-title":"Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images","volume":"39","author":"Kim","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2728","DOI":"10.1016\/j.ultrasmedbio.2014.06.006","article-title":"Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search","volume":"40","author":"Ni","year":"2014","journal-title":"Ultrasound Med. Biol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/JBHI.2015.2425041","article-title":"Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks","volume":"19","author":"Chen","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7790","DOI":"10.3934\/mbe.2021387","article-title":"Automatic measurement of fetal femur length in ultrasound images: A comparison of random forest regression model and SegNet","volume":"18","author":"Zhu","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Van Den Heuvel, T.L.A., De Bruijn, D., De Korte, C.L., and Ginneken, B.V. (2018). Automated measurement of fetal head circumference using 2D ultrasound images. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0200412"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1161\/01.cir.0000437597.44550.5d","article-title":"Diagnosis and Treatment of Fetal Cardiac Disease: A Scientific Statement from the American Heart Association","volume":"129","author":"Donofrio","year":"2014","journal-title":"Circulation"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1002\/(SICI)1097-0223(199712)17:13<1283::AID-PD296>3.0.CO;2-T","article-title":"Prenatal diagnosis of congenital heart disease","volume":"17","author":"Gembruch","year":"1997","journal-title":"Prenat. Diagn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1002\/uog.12403","article-title":"ISUOG Practice Guidelines (updated): Sonographic screening examination of the fetal heart","volume":"41","author":"Carvalho","year":"2013","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1017\/S1047951116000639","article-title":"Neonatal management and outcomes of prenatally diagnosed CHDs","volume":"27","author":"Bensemlali","year":"2017","journal-title":"Cardiol. Young"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1038\/s41591-021-01342-5","article-title":"An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease","volume":"27","author":"Arnaout","year":"2021","journal-title":"Nat. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1002\/uog.12563","article-title":"Fetal Intelligent Navigation Echocardiography (FINE): A novel method for rapid, simple, and automatic examination of the fetal heart: Fetal intelligent navigation echocardiography (FINE)","volume":"42","author":"Yeo","year":"2013","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1002\/jum.15105","article-title":"Semiautomatic Fetal Intelligent Navigation Echocardiography Has the Potential to Aid Cardiac Evaluations Even in Less Experienced Hands","volume":"39","author":"Gembicki","year":"2020","journal-title":"J. Ultrasound Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1007\/s10554-020-01932-3","article-title":"Diagnostic performance of fetal intelligent navigation echocardiography (FINE) in fetuses with double-outlet right ventricle (DORV)","volume":"36","author":"Ma","year":"2020","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Veronese, P., Guariento, A., Cattapan, C., Fedrigo, M., Gervasi, M.T., Angelini, A., Riva, A., and Vida, V. (2023). Prenatal Diagnosis and Fetopsy Validation of Complete Atrioventricular Septal Defects Using the Fetal Intelligent Navigation Echocardiography Method. Diagnostics, 13.","DOI":"10.3390\/diagnostics13030456"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Dozen, A., Komatsu, M., Sakai, A., Komatsu, R., Shozu, K., Machino, H., Yasutomi, S., Arakaki, T., Asada, K., and Kaneko, S. (2020). Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information. Biomolecules, 10.","DOI":"10.3390\/biom10111526"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Han, G., Jin, T., Zhang, L., Guo, C., Gui, H., Na, R., Wang, X., and Bai, H. (2022). Adoption of Compound Echocardiography under Artificial Intelligence Algorithm in Fetal Congenial Heart Disease Screening during Gestation. Appl. Bionics Biomech., 2022.","DOI":"10.1155\/2022\/6410103"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"101690","DOI":"10.1016\/j.compmedimag.2019.101690","article-title":"DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography","volume":"80","author":"Xu","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5650378","DOI":"10.1155\/2023\/5650378","article-title":"Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart","volume":"2023","author":"Wu","year":"2023","journal-title":"Comput. Math. Methods Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1515\/jpm-2023-0041","article-title":"Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning","volume":"51","author":"Yang","year":"2023","journal-title":"J. Perinat. Med."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Nurmaini, S., Rachmatullah, M.N., Sapitri, A.I., Darmawahyuni, A., Tutuko, B., Firdaus, F., Partan, R.U., and Bernolian, N. (2021). Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection. Sensors, 21.","DOI":"10.3390\/s21238007"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/TMI.2019.2946059","article-title":"Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning","volume":"39","author":"Gong","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Nurmaini, S., Partan, R.U., Bernolian, N., Sapitri, A.I., Tutuko, B., Rachmatullah, M.N., Darmawahyuni, A., Firdaus, F., and Mose, J.C. (2022). Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases. J. Clin. Med., 11.","DOI":"10.3390\/jcm11216454"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1002\/pd.6220","article-title":"Diagnosis of fetal total anomalous pulmonary venous connection based on the post-left atrium space ratio using artificial intelligence","volume":"42","author":"Wang","year":"2022","journal-title":"Prenat. Diagn."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4797315","DOI":"10.1155\/2017\/4797315","article-title":"Determination of Fetal Left Ventricular Volume Based on Two-Dimensional Echocardiography","volume":"2017","author":"Yu","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1002\/uog.23703","article-title":"Automated quantitative evaluation of fetal atrioventricular annular plane systolic excursion","volume":"58","author":"Herling","year":"2021","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Scharf, J.L., Dracopoulos, C., Gembicki, M., Welp, A., and Weichert, J. (2023). How Automated Techniques Ease Functional Assessment of the Fetal Heart: Applicability of MPI+TM for Direct Quantification of the Modified Myocardial Performance Index. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101705"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1109\/JBHI.2022.3143319","article-title":"A Pseudo-Siamese Feature Fusion Generative Adversarial Network for Synthesizing High-Quality Fetal Four-Chamber Views","volume":"27","author":"Qiao","year":"2023","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1109\/JBHI.2020.2973372","article-title":"Hierarchical Class Incremental Learning of Anatomical Structures in Fetal Echocardiography Videos","volume":"24","author":"Patra","year":"2020","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1002\/rcs.145","article-title":"Computer-assisted navigation applied to fetal cardiac intervention","volume":"3","author":"Emery","year":"2007","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1109\/JBHI.2019.2948316","article-title":"A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes","volume":"24","author":"Dong","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1515\/jpm-2023-0052","article-title":"Evaluation of an artificial intelligent algorithm (HeartassistTM) to automatically assess the quality of second trimester cardiac views: A prospective study","volume":"51","author":"Pietrolucci","year":"2023","journal-title":"J. Perinat. Med."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10877-012-9404-7","article-title":"Automated region mask for four-chamber fetal heart biometry","volume":"27","author":"Vijayalakshmi","year":"2013","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1007\/s11517-023-02814-1","article-title":"Performance evaluation of computer-aided automated master frame selection techniques for fetal echocardiography","volume":"61","author":"Sriraam","year":"2023","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1002\/uog.14940","article-title":"Evaluation of right ventricular function in fetal hypoplastic left heart syndrome by color tissue Doppler imaging: Right ventricular function in fetal HLHS","volume":"47","author":"Graupner","year":"2016","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e049640","DOI":"10.1136\/bmjopen-2021-049640","article-title":"Reference ranges of fetal heart function using a Modified Myocardial Performance Index: A prospective multicentre, cross-sectional study","volume":"11","author":"Sun","year":"2021","journal-title":"BMJ Open"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1007\/s11517-022-02753-3","article-title":"Automated multi-beat tissue Doppler echocardiography analysis using deep neural networks","volume":"61","author":"Lane","year":"2023","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"(2007). Sonographic examination of the fetal central nervous system: Guidelines for performing the \u2018basic examination\u2019 and the \u2018fetal neurosonogram\u2019. Ultrasound Obstet. Gynecol., 29, 109\u2013116.","DOI":"10.1002\/uog.3909"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"101998","DOI":"10.1016\/j.media.2021.101998","article-title":"Learning to map 2D ultrasound images into 3D space with minimal human annotation","volume":"70","author":"Yeung","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"119117","DOI":"10.1016\/j.neuroimage.2022.119117","article-title":"Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning","volume":"254","author":"Hesse","year":"2022","journal-title":"NeuroImage"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2018.02.006","article-title":"Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning","volume":"46","author":"Namburete","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/S0140-6736(14)61490-2","article-title":"International standards for fetal growth based on serial ultrasound measurements: The Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project","volume":"384","author":"Papageorghiou","year":"2014","journal-title":"Lancet"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/s11548-022-02609-z","article-title":"Deep learning-based plane pose regression in obstetric ultrasound","volume":"17","author":"Dromey","year":"2022","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_83","first-page":"1243","article-title":"A guiding approach of Ultrasound scan for accurately obtaining standard diagnostic planes of fetal brain malformation","volume":"30","author":"Yu","year":"2022","journal-title":"J. X-ray Sci. Technol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"057001","DOI":"10.1117\/1.JMI.7.5.057001","article-title":"Simulating realistic fetal neurosonography images with appearance and growth change using cycle-consistent adversarial networks and an evaluation","volume":"7","author":"Xu","year":"2020","journal-title":"J. Med. Imaging"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Lin, Q., Zhou, Y., Shi, S., Zhang, Y., Yin, S., Liu, X., Peng, Q., Huang, S., Jiang, Y., and Cui, C. (2022). How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10\u201314 in ultrasound using deep learning. Comput. Methods Programs Biomed., 226.","DOI":"10.1016\/j.cmpb.2022.107170"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1002\/uog.24843","article-title":"Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations","volume":"59","author":"Lin","year":"2022","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.media.2019.02.007","article-title":"Evaluating reinforcement learning agents for anatomical landmark detection","volume":"53","author":"Alansary","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1002\/jum.15860","article-title":"Machine Learning Algorithms for Classification of First-Trimester Fetal Brain Ultrasound Images","volume":"41","author":"Gofer","year":"2022","journal-title":"J. Ultrasound Med."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.media.2018.04.004","article-title":"VP-Nets: Efficient automatic localization of key brain structures in 3D fetal neurosonography","volume":"47","author":"Huang","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"101548","DOI":"10.1016\/j.media.2019.101548","article-title":"Multi-task learning for quality assessment of fetal head ultrasound images","volume":"58","author":"Lin","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1016\/j.ultrasmedbio.2017.07.013","article-title":"A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints","volume":"43","author":"Yaqub","year":"2017","journal-title":"Ultrasound Med. Biol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.radi.2020.11.006","article-title":"Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison","volume":"27","author":"Skelton","year":"2021","journal-title":"Radiography"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1007\/s11548-020-02182-3","article-title":"Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks","volume":"15","author":"Xie","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/03091902.2019.1653389","article-title":"Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images","volume":"43","author":"Sahli","year":"2019","journal-title":"J. Med. Eng. Technol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"100462","DOI":"10.1016\/j.ajogmf.2021.100462","article-title":"Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age","volume":"3","author":"Eixarch","year":"2021","journal-title":"Am. J. Obstet. Gynecol. MFM"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Sreelakshmy, R., Titus, A., Sasirekha, N., Logashanmugam, E., Begam, R.B., Ramkumar, G., and Raju, R. (2022). An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images. BioMed Res. Int., 2022.","DOI":"10.1155\/2022\/8342767"},{"key":"ref_97","first-page":"656942","article-title":"Recognition of Fetal Facial Ultrasound Standard Plane Based on Texture Feature Fusion","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/JBHI.2017.2705031","article-title":"A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition","volume":"22","author":"Yu","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Tang, J., Han, J., Xie, B., Xue, J., Zhou, H., Jiang, Y., Hu, L., Chen, C., Zhang, K., and Zhu, F. (2023). The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20032377"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1111\/jog.13099","article-title":"Current status of fetal neurodevelopmental assessment: Four-dimensional ultrasound study: Fetal neurodevelopmental assessment","volume":"42","author":"Hata","year":"2016","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1515\/jpm-2016-0377","article-title":"Fetal face as important indicator of fetal brain function","volume":"45","author":"AboEllail","year":"2017","journal-title":"J. Perinat. Med."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1002\/ijgo.14569","article-title":"Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions","volume":"161","author":"Miyagi","year":"2023","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1515\/jpm-2020-0537","article-title":"Recognition of facial expression of fetuses by artificial intelligence (AI)","volume":"49","author":"Miyagi","year":"2021","journal-title":"J. Perinat. Med."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1515\/jpm-2023-0092","article-title":"Fetal brain activity and the free energy principle","volume":"51","author":"Miyagi","year":"2023","journal-title":"J. Perinat. Med."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.placenta.2020.05.003","article-title":"The placenta in fetal growth restriction: What is going wrong?","volume":"96","author":"Sun","year":"2020","journal-title":"Placenta"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1146\/annurev-cellbio-100814-125620","article-title":"Placenta: The Forgotten Organ","volume":"31","author":"Maltepe","year":"2015","journal-title":"Annu. Rev. Cell Dev. Biol."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s00404-020-05601-7","article-title":"Early sonographic evaluation of the placenta in cases with IUGR: A pilot study","volume":"302","author":"Walter","year":"2020","journal-title":"Arch. Gynecol. Obstet."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Hu, R., Singla, R., Yan, R., Mayer, C., and Rohling, R.N. (2019, January 23\u201327). Automated Placenta Segmentation with a Convolutional Neural Network Weighted by Acoustic Shadow Detection. Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany. Available online: https:\/\/ieeexplore.ieee.org\/document\/8857448\/.","DOI":"10.1109\/EMBC.2019.8857448"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1038\/s41598-023-29105-x","article-title":"Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization","volume":"13","author":"Andreasen","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.ultrasmedbio.2021.12.006","article-title":"Automatic Placenta Localization from Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning","volume":"48","author":"Schilpzand","year":"2022","journal-title":"Ultrasound Med. Biol."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1159\/000324318","article-title":"Placental Volume at 11\u201313 Weeks\u2019 Gestation in the Prediction of Birth Weight Percentile","volume":"30","author":"Plasencia","year":"2011","journal-title":"Fetal Diagn. Ther."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1002\/jum.15835","article-title":"Fully Automated Placental Volume Quantification From 3D Ultrasound for Prediction of Small-for-Gestational-Age Infants","volume":"41","author":"Schwartz","year":"2022","journal-title":"J. Ultrasound Med."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1109\/TUFFC.2021.3052143","article-title":"Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment","volume":"68","author":"Looney","year":"2021","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_114","first-page":"921","article-title":"Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation","volume":"Volume 11071","author":"Frangi","year":"2018","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018, Proceedings of the 1st International Conference, Granada, Spain, 16\u201320 September 2018"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/978-3-319-60964-5_9","article-title":"Weakly Supervised Learning of Placental Ultrasound Images with Residual Networks","volume":"Volume 723","year":"2017","journal-title":"Medical Image Understanding and Analysis"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"12818","DOI":"10.1038\/srep12818","article-title":"Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector","volume":"5","author":"Lei","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"2821","DOI":"10.3233\/BME-141100","article-title":"Automatic staging of placental maturity based on dense descriptor","volume":"24","author":"Li","year":"2014","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"5587","DOI":"10.1080\/14767058.2021.1887847","article-title":"Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy","volume":"35","author":"Gupta","year":"2022","journal-title":"J. Matern. Neonatal Med."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.preghy.2022.12.003","article-title":"Multimodal fusion model for classifying placenta ultrasound imaging in pregnancies with hypertension disorders","volume":"31","author":"Sun","year":"2023","journal-title":"Pregnancy Hypertens."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1002\/jcu.23233","article-title":"Model application to quantitatively evaluate placental features from ultrasound images with gestational diabetes","volume":"50","author":"Sun","year":"2022","journal-title":"J. Clin. Ultrasound"},{"key":"ref_121","first-page":"3452176","article-title":"Deep Learning Algorithm-Based Ultrasound Image Information in Diagnosis and Treatment of Pernicious Placenta Previa","volume":"2022","author":"Yang","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"e13577","DOI":"10.1016\/j.heliyon.2023.e13577","article-title":"Automated placental abruption identification using semantic segmentation, quantitative features, SVM, ensemble and multi-path CNN","volume":"9","author":"Asadpour","year":"2023","journal-title":"Heliyon"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"146045822210842","DOI":"10.1177\/14604582221084211","article-title":"Machine learning model for umbilical cord classification using combination coiling index and texture feature based on 2-D Doppler ultrasound images","volume":"28","author":"Pradipta","year":"2022","journal-title":"Health Inform. J."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/0720-048X(96)01067-4","article-title":"An automated intelligent diagnostic system for the interpretation of umbilical artery Doppler velocimetry","volume":"23","author":"Egemen","year":"1996","journal-title":"Eur. J. Radiol."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0301-2115(95)02256-2","article-title":"A computerized diagnostic system for the interpretation of umbilical artery blood flow velocity waveforms","volume":"64","author":"Eskiizmirliler","year":"1996","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_126","unstructured":"Baykal, N., A Reggia, J., Yalabik, N., Erkmen, A., and Beksac, M.S. (1994). Interpretation of Doppler blood flow velocity waveforms using neural networks. Proc. Annu. Symp. Comput. Appl. Med. Care, 865\u2013869."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.acra.2019.11.006","article-title":"Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound","volume":"28","author":"Monill","year":"2021","journal-title":"Acad. Radiol."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Torrents-Barrena, J., L\u00f3pez-Velazco, R., Piella, G., Masoller, N., Valenzuela-Alcaraz, B., Gratac\u00f3s, E., Eixarch, E., Ceresa, M., and Ballester, M.G. (2019). TTTS-GPS: Patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery. Comput. Methods Programs Biomed., 179.","DOI":"10.1016\/j.cmpb.2019.104993"},{"key":"ref_129","unstructured":"Nicolaides, K.H. (2004). The 11\u201313+6 Weeks Scan, Fetal Medicine Foundation."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1002\/uog.12342","article-title":"ISUOG Practice Guidelines: Performance of first-trimester fetal ultrasound scan","volume":"41","author":"Salomon","year":"2013","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/S0140-6736(97)11280-6","article-title":"UK multicentre project on assessment of risk of trisomy 21 by maternal age and fetal nuchal-translucency thickness at 10\u201314 weeks of gestation","volume":"352","author":"Snijders","year":"1998","journal-title":"Lancet"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Walker, M.C., Willner, I., Miguel, O.X., Murphy, M.S.Q., El-Cha\u00e2r, D., Moretti, F., Harvey, A.L.J.D., White, R.R., Muldoon, K.A., and Carrington, A.M. (2022). Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0269323"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"e2217854","DOI":"10.1001\/jamanetworkopen.2022.17854","article-title":"Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester from Nuchal Ultrasonographic Images","volume":"5","author":"Zhang","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.compbiomed.2017.01.008","article-title":"Automatic detection and measurement of nuchal translucency","volume":"82","author":"Sciortino","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.compbiomed.2012.04.002","article-title":"A hierarchical model for automatic nuchal translucency detection from ultrasound images","volume":"42","author":"Deng","year":"2012","journal-title":"Comput. Biol. Med."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Tsai, P.-Y., Hung, C.-H., Chen, C.-Y., and Sun, Y.-N. (2020). Automatic Fetal Middle Sagittal Plane Detection in Ultrasound Using Generative Adversarial Network. Diagnostics, 11.","DOI":"10.3390\/diagnostics11010021"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Ryou, H., Yaqub, M., Cavallaro, A., Papageorghiou, A.T., and Noble, J.A. (2019). Automated 3D ultrasound image analysis for first trimester assessment of fetal health. Phys. Med. Biol., 64.","DOI":"10.1088\/1361-6560\/ab3ad1"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/TMI.2018.2858779","article-title":"Towards Automated Semantic Segmentation in Prenatal Volumetric Ultrasound","volume":"38","author":"Yang","year":"2019","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1002\/uog.8831","article-title":"Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan","volume":"37","author":"Salomon","year":"2010","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1002\/pd.6059","article-title":"Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time","volume":"42","author":"Matthew","year":"2022","journal-title":"Prenat. Diagn."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"9280","DOI":"10.1038\/s41598-020-66468-x","article-title":"A Nature-Inspired Search Space Reduction Technique for Spine Identification on Ultrasound Samples of Spina Bifida Cases","volume":"10","author":"Cengizler","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10916-019-1324-3","article-title":"Segmentation and Boundary Detection of Fetal Kidney Images in Second and Third Trimesters Using Kernel-Based Fuzzy Clustering","volume":"43","author":"Meenakshi","year":"2019","journal-title":"J. Med Syst."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Shozu, K., Komatsu, M., Sakai, A., Komatsu, R., Dozen, A., Machino, H., Yasutomi, S., Arakaki, T., Asada, K., and Kaneko, S. (2020). Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos. Biomolecules, 10.","DOI":"10.3390\/biom10121691"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"e2248685","DOI":"10.1001\/jamanetworkopen.2022.48685","article-title":"Development of a Machine Learning Model for Sonographic Assessment of Gestational Age","volume":"6","author":"Lee","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1038\/s43856-022-00194-5","article-title":"A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment","volume":"2","author":"Gomes","year":"2022","journal-title":"Commun. Med."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"223","DOI":"10.3233\/THC-1996-4209","article-title":"An intelligent diagnostic system for the assessment of gestational age based on ultrasonic fetal head measurements","volume":"4","author":"Egemen","year":"1996","journal-title":"Technol. Health Care"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.media.2014.12.006","article-title":"Learning-based prediction of gestational age from ultrasound images of the fetal brain","volume":"21","author":"Namburete","year":"2015","journal-title":"Med. Image Anal."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Alzubaidi, M., Agus, M., Shah, U., Makhlouf, M., Alyafei, K., and Househ, M. (2022). Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction. Diagnostics, 12.","DOI":"10.3390\/diagnostics12092229"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"102453","DOI":"10.1016\/j.artmed.2022.102453","article-title":"DeepGA for automatically estimating fetal gestational age through ultrasound imaging","volume":"135","author":"Dan","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1038\/s41746-023-00774-2","article-title":"Machine learning for accurate estimation of fetal gestational age based on ultrasound images","volume":"6","author":"Lee","year":"2023","journal-title":"NPJ Digit. Med."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JMI.7.1.014501","article-title":"Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis","volume":"7","author":"Maraci","year":"2020","journal-title":"J. Med. Imaging"},{"key":"ref_152","first-page":"EVIDoa2100058","article-title":"AI Estimation of Gestational Age from Blind Ultrasound Sweeps in Low-Resource Settings","volume":"1","author":"Pokaprakarn","year":"2022","journal-title":"NEJM \u00c9vid."},{"key":"ref_153","unstructured":"Landman, B.A., and I\u0161gum, I. (2021). Medical Imaging 2021: Image Processing, SPIE. Available online: https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/11596\/2582243\/An-automated-framework-for-image-classification-and-segmentation-of-fetal\/10.1117\/12.2582243.full."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"14109","DOI":"10.1038\/s41598-021-92829-1","article-title":"Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video","volume":"11","author":"Drukker","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"101973","DOI":"10.1016\/j.media.2021.101973","article-title":"Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos","volume":"69","author":"Sharma","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1007\/s11548-022-02642-y","article-title":"Task model-specific operator skill assessment in routine fetal ultrasound scanning","volume":"17","author":"Wang","year":"2022","journal-title":"Int. J. CARS"},{"key":"ref_157","first-page":"670","article-title":"Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning Using Landmark Retrieval","volume":"Volume 12908","author":"Cattin","year":"2021","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021, Proceedings of the 24th International Conference, Strasbourg, France, 27 September\u20131 October 2021"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1002\/uog.24975","article-title":"Clinical workflow of sonographers performing fetal anomaly ultrasound scans: Deep-learning-based analysis","volume":"60","author":"Drukker","year":"2022","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"102630","DOI":"10.1016\/j.media.2022.102630","article-title":"Gaze-assisted automatic captioning of fetal ultrasound videos using three-way multi-modal deep neural networks","volume":"82","author":"Alsharid","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Sharma, H., Drukker, L., Papageorghiou, A.T., and Noble, J.A. (2021, January 13\u201316). Multi-Modal Learning from Video, Eye Tracking, and Pupillometry for Operator Skill Characterization in Clinical Fetal Ultrasound. Proceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France. Available online: https:\/\/ieeexplore.ieee.org\/document\/9433863\/.","DOI":"10.1109\/ISBI48211.2021.9433863"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1097\/CM9.0000000000001547","article-title":"Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation","volume":"134","author":"Xia","year":"2021","journal-title":"Chin. Med. J."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1002\/uog.22037","article-title":"Application of ultrasound-based radiomics technology in fetal-lung-texture analysis in pregnancies complicated by gestational diabetes and\/or pre-eclampsia","volume":"57","author":"Du","year":"2021","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1007\/s11548-020-02211-1","article-title":"A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images","volume":"15","author":"Chen","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"12747","DOI":"10.1038\/s41598-022-17129-8","article-title":"Ultrasound-based radiomics technology in fetal lung texture analysis prediction of neonatal respiratory morbidity","volume":"12","author":"Du","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1002\/uog.13441","article-title":"Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity","volume":"45","author":"Palacio","year":"2015","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/TITB.2004.832548","article-title":"A Novel Algorithm for Computer-Assisted Measurement of Cervical Length from Transvaginal Ultrasound Images","volume":"8","author":"Wu","year":"2004","journal-title":"IEEE Trans. Inform. Technol. Biomed."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"9937051","DOI":"10.1155\/2022\/9937051","article-title":"B-Ultrasound Image Analysis of Intrauterine Pregnancy Residues after Mid-Term Pregnancy Based on Smart Medical Big Data","volume":"2022","author":"He","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_168","first-page":"4010339","article-title":"Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm","volume":"2022","author":"Wang","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_169","first-page":"4405189","article-title":"Doppler Ultrasound Imaging Combined with Fetal Heart Detection in Predicting Fetal Distress in Pregnancy-Induced Hypertension under the Guidance of Artificial Intelligence Algorithm","volume":"2021","author":"Liu","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Q., Yin, C., Chen, L., Yang, Z., Jia, S., Sun, X., Bai, Y., Han, F., and Yuan, Z. (2022). Automated prediction of early spontaneous miscarriage based on the analyzing ultrasonographic gestational sac imaging by the convolutional neural network: A case-control and cohort study. BMC Pregnancy Childbirth, 22.","DOI":"10.1186\/s12884-022-04936-0"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1016\/j.ultrasmedbio.2010.03.006","article-title":"A Novel Technique for the Semi-Automated Measurement of Embryo Volume: An Intraobserver Reliability Study","volume":"36","author":"Sur","year":"2010","journal-title":"Ultrasound Med. Biol."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/uog.23739","article-title":"Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor","volume":"59","author":"Ghi","year":"2021","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"5192338","DOI":"10.1155\/2022\/5192338","article-title":"Multitask Deep Neural Network for the Fully Automatic Measurement of the Angle of Progression","volume":"2022","author":"Lu","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"940150","DOI":"10.3389\/fphys.2022.940150","article-title":"A framework for computing angle of progression from transperineal ultrasound images for evaluating fetal head descent using a novel double branch network","volume":"13","author":"Bai","year":"2022","journal-title":"Front. Physiol."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TCYB.2017.2671898","article-title":"FUIQA: Fetal Ultrasound Image Quality Assessment with Deep Convolutional Networks","volume":"47","author":"Wu","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"2755","DOI":"10.1109\/TMI.2019.2913311","article-title":"Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging","volume":"38","author":"Meng","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Gupta, L., Sisodia, R.S., Pallavi, V., Firtion, C., and Ramachandran, G. (September, January 30). Segmentation of 2D fetal ultrasound images by exploiting context information using conditional random fields. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091824"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"1786994","DOI":"10.1155\/2022\/1786994","article-title":"Evaluation of Nursing Effect of Pelvic Floor Rehabilitation Training on Pelvic Organ Prolapse in Postpartum Pregnant Women under Ultrasound Imaging with Artificial Intelligence Algorithm","volume":"2022","author":"Yin","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"101951","DOI":"10.1016\/j.media.2020.101951","article-title":"Automated ultrasound assessment of amniotic fluid index using deep learning","volume":"69","author":"Cho","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s13089-022-00283-5","article-title":"Artificial intelligence enhanced ultrasound (AI-US) in a severe obese parturient: A case report","volume":"14","author":"Compagnone","year":"2022","journal-title":"Ultrasound J."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.media.2015.07.002","article-title":"Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step","volume":"26","author":"Rueda","year":"2015","journal-title":"Med. Image Anal."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"6034971","DOI":"10.1155\/2022\/6034971","article-title":"PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images","volume":"2022","author":"Kaplan","year":"2022","journal-title":"Contrast Media Mol. Imaging"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1002\/uog.5365","article-title":"Simple ultrasound-based rules for the diagnosis of ovarian cancer","volume":"31","author":"Timmerman","year":"2008","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"285","DOI":"10.7863\/jum.2009.28.3.285","article-title":"Gynecologic Imaging Reporting and Data System: A New Proposal for Classifying Adnexal Masses on the Basis of Sonographic Findings","volume":"28","author":"Amor","year":"2009","journal-title":"J. Ultrasound Med."},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Hsu, S.-T., Su, Y.-J., Hung, C.-H., Chen, M.-J., Lu, C.-H., and Kuo, C.-E. (2022). Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-02047-6"},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1177\/0161734621998091","article-title":"An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses","volume":"43","author":"Du","year":"2021","journal-title":"Ultrason. Imaging"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.ultrasmedbio.2015.11.014","article-title":"Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach","volume":"42","author":"Cabeza","year":"2016","journal-title":"Ultrasound Med. Biol."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1002\/uog.23530","article-title":"Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: Comparison with expert subjective assessment","volume":"57","author":"Christiansen","year":"2021","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"e179","DOI":"10.1016\/S2589-7500(21)00278-8","article-title":"Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: A retrospective, multicentre, diagnostic study","volume":"4","author":"Gao","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"17024","DOI":"10.1038\/s41598-022-20653-2","article-title":"Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder","volume":"12","author":"Jung","year":"2022","journal-title":"Sci Rep."},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-M\u00e1s, J., Bueno-Crespo, A., Khazendar, S., Remezal-Solano, M., Mart\u00ednez-Cend\u00e1n, J.-P., Jassim, S., Du, H., Al Assam, H., Bourne, T., and Timmerman, D. (2019). Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0219388"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1148\/radiol.211367","article-title":"Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment","volume":"304","author":"Chen","year":"2022","journal-title":"Radiology"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"16511","DOI":"10.1038\/s41598-020-73505-2","article-title":"Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: A hypothesis generating study (PROBE study)","volume":"10","author":"Nero","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"Chen, L., Qiao, C., Wu, M., Cai, L., Yin, C., Yang, M., Sang, X., and Bai, W. (2023). Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020184"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.breast.2022.04.003","article-title":"Global guidelines for breast cancer screening: A systematic review","volume":"64","author":"Ren","year":"2022","journal-title":"Breast"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"e223351","DOI":"10.1148\/radiol.223351","article-title":"Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting","volume":"307","author":"Berg","year":"2023","journal-title":"Radiology"},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"1098639","DOI":"10.3389\/fpubh.2022.1098639","article-title":"Artificial intelligence breast ultrasound and handheld ultrasound in the BI-RADS categorization of breast lesions: A pilot head to head comparison study in screening program","volume":"10","author":"Huang","year":"2023","journal-title":"Front. Public Health"},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Browne, J.L., Pascual, M.\u00c1., Perez, J., Salazar, S., Valero, B., Rodriguez, I., Cassina, D., Alcazar, J.L., Guerriero, S., and Graupera, B. (2023). AI: Can It Make a Difference to the Predictive Value of Ultrasound Breast Biopsy?. Diagnostics, 13.","DOI":"10.3390\/diagnostics13040811"},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejca.2022.09.018","article-title":"Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): A retrospective, international, multicentre analysis","volume":"177","author":"Pfob","year":"2022","journal-title":"Eur. J. Cancer"},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"4991","DOI":"10.1007\/s00330-020-07561-7","article-title":"One step further into the blackbox: A pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound","volume":"31","author":"Dong","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"4101","DOI":"10.1007\/s00330-021-08519-z","article-title":"The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): An international, multicenter analysis","volume":"32","author":"Pfob","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1002\/uog.24910","article-title":"Ultrasound features of endometrial pathology in women without abnormal uterine bleeding: Results from the International Endometrial Tumor Analysis study (IETA3)","volume":"60","author":"Heremans","year":"2022","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.ajog.2022.07.043","article-title":"Risk of endometrial cancer in asymptomatic postmenopausal women in relation to ultrasonographic endometrial thickness: Systematic review and diagnostic test accuracy meta-analysis","volume":"228","author":"Vitale","year":"2022","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.21037\/qims-22-965","article-title":"Artificial intelligence diagnosis of intrauterine adhesion by 3D ultrasound imaging: A prospective study","volume":"13","author":"Zhao","year":"2023","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.21037\/qims-21-1155","article-title":"Automatic evaluation of endometrial receptivity in three-dimensional transvaginal ultrasound images based on 3D U-Net segmentation","volume":"12","author":"Wang","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"3974","DOI":"10.1002\/mp.13677","article-title":"Endometrium segmentation on transvaginal ultrasound image using key-point discriminator","volume":"46","author":"Park","year":"2019","journal-title":"Med. Phys."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhou, Q., Peng, B., Jiang, J., Fang, L., Weng, W., Wang, W., Wang, S., and Zhu, X. (2022). Automatic Measurement of Endometrial Thickness from Transvaginal Ultrasound Images. Front. Bioeng. Biotechnol., 10.","DOI":"10.3389\/fbioe.2022.853845"},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1002\/uog.24805","article-title":"Developing and validating ultrasound-based radiomics models for predicting high-risk endometrial cancer","volume":"60","author":"Moro","year":"2022","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"1116332","DOI":"10.1155\/2022\/1116332","article-title":"Ultrasound Evaluation of Pelvic Floor Function after Transumbilical Laparoscopic Single-Site Total Hysterectomy Using Deep Learning Algorithm","volume":"2022","author":"Zhu","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"3470","DOI":"10.1016\/j.ultrasmedbio.2021.08.009","article-title":"Automatic Extraction of Hiatal Dimensions in 3-D Transperineal Pelvic Ultrasound Recordings","volume":"47","author":"Williams","year":"2021","journal-title":"Ultrasound Med. Biol."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"6215","DOI":"10.1002\/mp.16389","article-title":"Automated segmentation and measurement of the female pelvic floor from the mid-sagittal plane of 3D ultrasound volumes","volume":"50","author":"Szentimrey","year":"2023","journal-title":"Med. Phys."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1002\/uog.24810","article-title":"Automatic identification and segmentation of slice of minimal hiatal dimensions in transperineal ultrasound volumes","volume":"60","author":"Manzini","year":"2022","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1002\/uog.20181","article-title":"Deep learning enables automatic quantitative assessment of puborectalis muscle and urogenital hiatus in plane of minimal hiatal dimensions","volume":"54","author":"Grob","year":"2019","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"5898","DOI":"10.1007\/s00330-022-08754-y","article-title":"Development and validation of a composite AI model for the diagnosis of levator ani muscle avulsion","volume":"32","author":"Wu","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1530\/RAF-21-0031","article-title":"Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign","volume":"2","author":"Maicas","year":"2021","journal-title":"Reprod. Fertil."},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Raimondo, D., Raffone, A., Aru, A.C., Giorgi, M., Giaquinto, I., Spagnolo, E., Travaglino, A., Galatolo, F.A., Cimino, M.G.C.A., and Lenzi, J. (2023). Application of Deep Learning Model in the Sonographic Diagnosis of Uterine Adenomyosis. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20031724"},{"key":"ref_218","first-page":"3269692","article-title":"Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure","volume":"2022","author":"Zhang","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_219","first-page":"2645607","article-title":"Diagnosis of Idiopathic Premature Ovarian Failure by Color Doppler Ultrasound under the Intelligent Segmentation Algorithm","volume":"2022","author":"Yu","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.1038\/s41598-022-26771-1","article-title":"Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: A retrospective study","volume":"13","author":"Huo","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1016\/j.ultrasmedbio.2023.03.013","article-title":"Real-Time Automatic Assisted Detection of Uterine Fibroid in Ultrasound Images Using a Deep Learning Detector","volume":"49","author":"Yang","year":"2023","journal-title":"Ultrasound Med. Biol."},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"Singh, V.K., Yousef Kalafi, E., Cheah, E., Wang, S., Wang, J., Ozturk, A., Li, Q., Eldar, Y.C., Samir, A.E., and Kumar, V. (2022). HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy. Diagnostics, 12.","DOI":"10.3390\/diagnostics12123213"},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"296","DOI":"10.4103\/jhrs.JHRS_91_20","article-title":"Three-Dimensional Automated Volume Calculation (Sonography-Based Automated Volume Count) versus Two-Dimensional Manual Ultrasonography for Follicular Tracking and Oocyte Retrieval in Women Undergoing in vitro Fertilization-Embryo Transfer: A Randomized Controlled Trial","volume":"13","author":"Noor","year":"2020","journal-title":"J. Hum. Reprod. Sci."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.jogoh.2017.03.004","article-title":"Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology","volume":"46","author":"Maurice","year":"2017","journal-title":"J. Gynecol. Obstet. Hum. Reprod."},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Dhombres, F., Maurice, P., Friszer, S., Guilbaud, L., Lelong, N., Khoshnood, B., Charlet, J., Perrot, N., Jauniaux, E., and Jurkovic, D. (2017). Developing a knowledge base to support the annotation of ultrasound images of ectopic pregnancy. J. Biomed. Semant., 8.","DOI":"10.1186\/s13326-017-0117-1"},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"102651","DOI":"10.1016\/j.media.2022.102651","article-title":"Tunable image quality control of 3-D ultrasound using switchable CycleGAN","volume":"83","author":"Huh","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1111\/j.1749-6632.2000.tb06251.x","article-title":"Premature ovarian failure is not premature menopause","volume":"900","author":"Kalantaridou","year":"2006","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13244-023-01376-y","article-title":"Reproducibility of 2D versus 3D radiomics for quantitative assessment of fetal lung development: A retrospective fetal MRI study","volume":"14","author":"Watzenboeck","year":"2023","journal-title":"Insights Imaging"},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"4205","DOI":"10.1007\/s00330-022-09367-1","article-title":"Fetal MRI radiomics: Non-invasive and reproducible quantification of human lung maturity","volume":"33","author":"Prayer","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1002\/jmri.26192","article-title":"Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation: Radiomic Features in Uterine Cervical Cancer","volume":"49","author":"Liu","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1097\/OGX.0000000000000910","article-title":"Introduction to Artificial Intelligence in Ultrasound Imaging in Obstetrics and Gynecology","volume":"76","author":"Drukker","year":"2021","journal-title":"Obstet. Gynecol. Surv."},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1002\/uog.4051","article-title":"Assessment of lung area in fetuses with congenital diaphragmatic hernia","volume":"30","author":"Jani","year":"2007","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_233","unstructured":"World Health Organization (2021). Ethics and Governance of Artificial Intelligence for Health: WHO Guidance, WHO. Licence: CC BY-NC-SA 3.0 IGO."},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"e35465","DOI":"10.2196\/35465","article-title":"Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review","volume":"24","author":"Dhombres","year":"2022","journal-title":"J. Med. Internet Res."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/12\/21\/6833\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:13:46Z","timestamp":1760130826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/12\/21\/6833"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,29]]},"references-count":234,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["jcm12216833"],"URL":"https:\/\/doi.org\/10.3390\/jcm12216833","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,29]]}}}