{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:59:36Z","timestamp":1792789176951,"version":"4.5.0"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Social Fund Plus","award":["350696"],"award-info":[{"award-number":["350696"]}],"id":[{"id":"https:\/\/ror.org\/048snh465","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Artificial intelligence (AI)-assisted automatic fetal measurements during ultrasound evaluation have become a major subject of interest. Most publications include systems developed and tested for a single anatomical component. In routine clinical practice, multiple biometric parameters are required for fetal weight estimation. Our objective is to assess the studies that incorporated three or more standard plane fetal automatic measurements with the help of AI systems in the second and third trimesters. Methods: We performed a systematic review of the published research focusing on automated AI systems analyzing at least three standard fetal biometric parameters or their corresponding standard ultrasound planes for fetal biometric, gestational-age, or fetal-growth assessment. The literature screening had to meet the predefined inclusion and exclusion criteria. Results: Nineteen studies, comprising more than 730,000 ultrasound images, met the inclusion criteria, and eighteen studies compared automated measurements with manual reference measurements. Overall, the included studies reported favourable performance for automated assessment of biparietal diameter, head circumference, abdominal circumference, and femur length, although substantial heterogeneity was observed in study design, AI architecture, validation methodology, and reported performance metrics. Conclusions: Automated AI systems show promising performance for assessing multiple standard fetal biometric parameters and may improve measurement consistency and examination efficiency. However, substantial heterogeneity in datasets, model architectures, and outcome reporting restricts direct comparison and the certainty of the evidence. Standardized reporting and prospective multicentre evaluation across gestational ages (GA), ultrasound systems, and operators are needed before these systems can be recommended for routine clinical use.<\/jats:p>","DOI":"10.3390\/diagnostics16183063","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T12:50:03Z","timestamp":1789995003000},"page":"3063","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Systematic Review of the Use of Artificial Intelligence Systems for Assessing Multiple Parameters During the Ultrasound Evaluation of Fetal Growth"],"prefix":"10.3390","volume":"16","author":[{"given":"Oana","family":"C\u00e2rlig","sequence":"first","affiliation":[{"name":"Doctoral School, University of Medicine and Pharmacy of Craiova, 20039 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7650-6710","authenticated-orcid":false,"given":"C\u0103t\u0103lina","family":"Iovoaica-R\u0103mescu","sequence":"additional","affiliation":[{"name":"Doctoral School, University of Medicine and Pharmacy of Craiova, 20039 Craiova, Romania"},{"name":"Department of Obstetrics and Gynecology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florin","family":"Claustel Jurma","sequence":"additional","affiliation":[{"name":"Facultatea de Informatic\u0103, Universitatea de Vest Timi\u0219oara, 300223 Timi\u0219oara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7823-3387","authenticated-orcid":false,"given":"Dominic Gabriel","family":"Iliescu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania"},{"name":"Department of Obstetrics and Gynecology, Emergency County Hospital of Craiova, 200642 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ref_1","unstructured":"Stryker, C., and Kavalakoglu, E. (2026, September 15). What is AI? Institute of Business Management. Available online: https:\/\/www.ibm.com\/think\/topics\/artificial-intelligence."},{"key":"ref_2","unstructured":"Jordan, M.I., and Mitchell, T.M. (2026, September 15). Machine Learning: Trends, Perspectives, and Prospects, Carnegie Mellon University. Available online: https:\/\/www.cs.cmu.edu\/~tom\/pubs\/Science-ML-2015.pdf."},{"key":"ref_3","unstructured":"Litjens, G., Kooi, T., Ehteshami Bejnordi, B., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., and van Ginneken, B. (2026, September 15). A Survey on Deep Learning in Medical Image Analysis. Available online: https:\/\/www.researchgate.net\/publication\/313857891_A_Survey_on_Deep_Learning_in_Medical_Image_Analysis."},{"key":"ref_4","unstructured":"Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J.N., Wu, Z., and Ding, X. (2026, September 15). Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation, Available online: https:\/\/pubmed.ncbi.nlm.nih.gov\/32289663\/."},{"key":"ref_5","unstructured":"Chan, H.-P., Hadjiiski, L.M., and Samala, R.K. (2026, September 15). Computer-Aided Diagnosis in the Era of Deep Learning, Available online: https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC7293164\/."},{"key":"ref_6","unstructured":"Wang, G., Zhang, Y., Ye, X., and Mou, X. (2019). Machine Learning for Tomographic Imaging, IOP Publishing. Available online: https:\/\/iopscience.iop.org\/book\/mono\/978-0-7503-2216-4?utm_medium=email&utm_source=iop&utm_term=&utm_campaign=18269-45648&utm_content=Download%20two%20free%20ebooks%20from%20IOP%20Publishing&Campaign+Owner=Laura+Gillham."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","article-title":"A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises","volume":"109","author":"Zhou","year":"2021","journal-title":"Proc. IEEE Inst. Electr. Electron. Eng."},{"key":"ref_8","first-page":"213","article-title":"A Short History of Sonography in Obstetrics and Gynaecology","volume":"5","author":"Campbell","year":"2013","journal-title":"Facts Views Vis. Obgyn"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1111\/j.1471-0528.2012.03451.x","article-title":"Systematic review of methodology used in ultrasound studies aimed at creating charts of fetal size","volume":"119","author":"Ioannou","year":"2012","journal-title":"BJOG"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1002\/uog.20272","article-title":"ISUOG Practice Guidelines: Ultrasound assessment of fetal biometry and growth","volume":"53","author":"Salomon","year":"2019","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"American Institute of Ultrasound in Medicine (2020). AIUM Practice Parameter for the Performance of Limited Obstetric Ultrasound Examinations by Advanced Clinical Providers. J. Ultrasound Med., 39, 2285.","DOI":"10.1002\/jum.15204"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1002\/uog.22000","article-title":"INTERGROWTH-21st standards for Hadlock\u2019s estimation of fetal weight","volume":"56","author":"Stirnemann","year":"2020","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1002\/uog.19066","article-title":"Ultrasonographic estimation of fetal weight: Development of new model and assessment of performance of previous models","volume":"52","author":"Hammami","year":"2018","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_15","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":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1056\/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 Evid."},{"key":"ref_17","first-page":"483","article-title":"Potential of Artificial Intelligence for Estimating Japanese Fetal Weights","volume":"74","author":"Miyagi","year":"2020","journal-title":"Acta Med. Okayama"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1007\/s11517-008-0407-y","article-title":"Fetal ultrasound image segmentation system and its use in fetal weight estimation","volume":"46","author":"Yu","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1002\/uog.26648","article-title":"EP02.31: BiometryAssist\u00ae versus manual approach for fetal biometry: Correlation, level of agreement and comparison of acquisition time","volume":"62","author":"Rolle","year":"2023","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_20","unstructured":"Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., and Naidu, R. (2020). Texture-Based Fuzzy Connectedness Algorithm for Fetal Ultrasound Image Segmentation for Biometric Measurements. Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, Springer."},{"key":"ref_21","unstructured":"Ibrahim, R., Porkumaran, K., Kannan, R., Mohd Nor, N., and Prabakar, S. (2022). Semi-Automatic Detection and Measurement of Fetal Parameters from Ultrasound Images and the Scope Automatic System Using LabVIEW. International Conference on Artificial Intelligence for Smart Community. Lecture Notes in Electrical Engineering, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"524","DOI":"10.38136\/jgon.796594","article-title":"Comparison of manual versus automated measurement to estimate fetal weight in isolated oligohydramnios with preterm prelabor rupture of membranes","volume":"17","author":"Eroglu","year":"2020","journal-title":"Jinekoloji-Obstet. Neonatoloji T\u0131p Derg."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1002\/ijgo.70041","article-title":"Volume-based complete automation for ultrasound fetal biometry: A pilot approach to assess feasibility, reliability, and perspectives","volume":"170","author":"Dap","year":"2025","journal-title":"Int. J. Gynaecol. Obstet."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, H., Hung, C.S., Lin, C.R., Yu, H.R., Zheng, Y.C., Yu, C.H., Tsai, C.M., and Huang, T.H. (2025). Predicting Fetal Growth with Curve Fitting and Machine Learning. Bioengineering, 12.","DOI":"10.3390\/bioengineering12070730"},{"key":"ref_25","unstructured":"Binti, S., Yusof, M., Tan, L., Werner, P., Abduljabbar, H., Pahl, C., Baigi, M., Tan, H., Ru, R., and Hussien, E. (2026, September 15). Fetal Weight Estimation using Canny Segmented Ultrasound Images. Available online: https:\/\/www.researchgate.net\/publication\/307970103_Fetal_Weight_Estimationusing_Canny_Segmented_Ultrasound_Images."},{"key":"ref_26","first-page":"115961N","article-title":"An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation","volume":"11596","author":"Prieto","year":"2021","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1038\/s41746-025-02024-z","article-title":"Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video","volume":"8","author":"Benson","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s00404-026-08351-0","article-title":"Impact of a new image enhancement technology on the biometric measurements in pregnancy","volume":"313","author":"Elger","year":"2026","journal-title":"Arch. Gynecol. Obstet."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mira, J., and Prieto, A. (2001). Foetal Age and Weight Determination Using a Lateral Interaction Inspired Net. Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/3-540-45723-2"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1002\/uog.12509","article-title":"Optimal caliper placement: Manual vs automated methods","volume":"43","author":"Yazdi","year":"2014","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103039","DOI":"10.1016\/j.jogoh.2025.103039","article-title":"Development and temporal validation of a deep learning model for automatic fetal biometry from ultrasound videos","volume":"54","author":"Haller","year":"2025","journal-title":"J. Gynecol. Obstet. Hum. Reprod."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1038\/s41746-025-01704-0","article-title":"Predicting abnormal fetal growth using deep learning","volume":"8","author":"Christensen","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"ref_33","unstructured":"Gon\u00e7alves, N., Oliveira, H.P., and S\u00e1nchez, J.A. (2025). AGE-US: Automated Gestational Age Estimation Based on Fetal Ultrasound Images. Pattern Recognition and Image Analysis. IbPRIA 2025. Lecture Notes in Computer Science, Springer."},{"key":"ref_34","first-page":"e100534","article-title":"A Modular Deep-Learning Framework for Automated Fetal Biometry: Proof-of-Concept Evaluation of Head, Abdominal, and Femur Measurements","volume":"17","author":"Sundaramoorthy","year":"2025","journal-title":"Cureus"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1038\/s41746-024-01406-z","article-title":"Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans","volume":"8","author":"Venturini","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"109000","DOI":"10.1016\/j.compbiomed.2024.109000","article-title":"Segmentation and Estimation of Fetal Biometric Parameters using an Attention Gate Double U-Net with Guided Decoder Architecture","volume":"180","author":"Degala","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Han, X., Yu, J., Yang, X., Chen, C., Zhou, H., Qiu, C., Cao, Y., Zhang, T., Peng, M., and Zhu, G. (2024). Artificial intelligence assistance for fetal development: Evaluation of an automated software for biometry measurements in the mid-trimester. BMC Pregnancy Childbirth, 24.","DOI":"10.1186\/s12884-024-06336-y"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"121482","DOI":"10.1016\/j.eswa.2023.121482","article-title":"A system-on-chip solution for deep learning-based automatic fetal biometric measurement","volume":"237","author":"Cho","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_39","unstructured":"Su, R., Zhang, Y.D., and Frangi, A.F. (2023). Multi-task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans. Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, Cambridge, UK, 9\u201310 December 2023, Springer."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7047","DOI":"10.1038\/s41467-023-42438-5","article-title":"Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning","volume":"14","author":"Slimani","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"103039","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_43","doi-asserted-by":"crossref","first-page":"045013","DOI":"10.1088\/1361-6560\/ac4d85","article-title":"Deep learning fetal ultrasound video model match human observers in biometric measurements","volume":"67","author":"Klasa","year":"2022","journal-title":"Phys. Med. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ejmp.2021.06.020","article-title":"Automatic fetal biometry prediction using a novel deep convolutional network architecture","volume":"88","author":"Shabanzadeh","year":"2021","journal-title":"Phys. Med."},{"key":"ref_45","unstructured":"Bertino, E., Gao, W., Steffen, B., and Yung, M. (2021). AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021. Lecture Notes in Computer Science, Springer."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1002\/uog.20185","article-title":"Evaluation of automated tool for two-dimensional fetal biometry","volume":"54","author":"Salim","year":"2019","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_47","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"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/3063\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T04:21:01Z","timestamp":1790137261000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/3063"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16183063"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16183063","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]}}}