{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:27:51Z","timestamp":1777879671083,"version":"3.51.4"},"reference-count":66,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014869","name":"Army Medical University Xinqiao Hospital","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014869","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110323","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T12:45:52Z","timestamp":1776516352000},"page":"110323","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["AortaQA: Fetal congenital heart disease screening system based on the anatomically-Guided Dual-Quality control"],"prefix":"10.1016","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9158-1105","authenticated-orcid":false,"given":"Yuxuan","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Jiajie","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Fanfan","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yuzhou","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Wanqi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hongying","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Long","family":"Lu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110323_b0005","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.acvd.2010.06.006","article-title":"The ductus arteriosus: Physiology, regulation, and functional and congenital anomalies","volume":"104","author":"Gournay","year":"2011","journal-title":"Arch. Cardiovasc. Dis."},{"key":"10.1016\/j.bspc.2026.110323_b0010","doi-asserted-by":"crossref","first-page":"F33","DOI":"10.1136\/adc.2007.119032","article-title":"Twenty-year trends in diagnosis of life-threatening neonatal cardiovascular malformations","volume":"93","author":"Wren","year":"2008","journal-title":"Arch. Dis. Child. - Fetal Neonatal Ed."},{"key":"10.1016\/j.bspc.2026.110323_b0015","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1113\/jphysiol.2009.174870","article-title":"Dynamic changes in the direction of blood flow through the ductus arteriosus at birth","volume":"587","author":"Crossley","year":"2009","journal-title":"J. Physiol."},{"key":"10.1016\/j.bspc.2026.110323_b0020","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1542\/peds.103.4.743","article-title":"Failure to diagnose congenital heart disease in infancy","volume":"103","author":"Kuehl","year":"1999","journal-title":"Pediatrics"},{"key":"10.1016\/j.bspc.2026.110323_b0025","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.siny.2013.04.007","article-title":"Diagnosis and management of life-threatening cardiac malformations in the newborn","volume":"18","author":"Mellander","year":"2013","journal-title":"Semin. Fetal Neonatal Med."},{"key":"10.1016\/j.bspc.2026.110323_b0030","doi-asserted-by":"crossref","unstructured":"A. de-Wahl Granelli, M. Wennergren, K. Sandberg, M. Mellander, C. Bejlum, L. Inganas, M. Eriksson, N. Segerdahl, A. Agren, B.-M. Ekman-Joelsson, J. Sunnegardh, M. Verdicchio, I. Ostman-Smith, Impact of pulse oximetry screening on the detection of duct dependent congenital heart disease: A Swedish prospective screening study in 39 821 newborns, BMJ 338 (2009) a3037\u2013a3037. https:\/\/doi.org\/10.1136\/bmj.a3037.","DOI":"10.1136\/bmj.a3037"},{"key":"10.1016\/j.bspc.2026.110323_b0035","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1161\/01.CIR.103.19.2376","article-title":"Mortality associated with congenital heart defects in the United States: Trends and racial disparities, 1979\u20131997","volume":"103","author":"Boneva","year":"2001","journal-title":"Circulation"},{"key":"10.1016\/j.bspc.2026.110323_b0040","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1542\/peds.2011-3211","article-title":"Endorsement of health and human services recommendation for pulse oximetry screening for critical congenital heart disease","volume":"129","author":"Mahle","year":"2012","journal-title":"Pediatrics"},{"key":"10.1016\/j.bspc.2026.110323_b0045","doi-asserted-by":"crossref","first-page":"e98","DOI":"10.1542\/peds.2013-2895","article-title":"Age at referral and mortality from critical congenital heart disease","volume":"134","author":"Fixler","year":"2014","journal-title":"Pediatrics"},{"key":"10.1016\/j.bspc.2026.110323_b0050","first-page":"1344","article-title":"Outcome of congenital heart defects - a population-based study","volume":"89","author":"Meberg","year":"2000","journal-title":"Acta Paediatr."},{"key":"10.1016\/j.bspc.2026.110323_b0055","doi-asserted-by":"crossref","DOI":"10.1002\/14651858.CD007058.pub3","article-title":"Ultrasound for fetal assessment in early pregnancy","author":"Whitworth","year":"2015","journal-title":"Cochrane Database Syst. Rev."},{"key":"10.1016\/j.bspc.2026.110323_b0060","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1056\/NEJM199309163291201","article-title":"Effect of prenatal ultrasound screening on perinatal outcome","volume":"329","author":"Ewigman","year":"1993","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.bspc.2026.110323_b0065","doi-asserted-by":"crossref","first-page":"164","DOI":"10.4103\/apc.APC_152_17","article-title":"Anatomy of the normal fetal heart: the basis for understanding fetal echocardiography","volume":"11","author":"Picazo-Angelin","year":"2018","journal-title":"Ann. Pediatr. Cardiol."},{"key":"10.1016\/j.bspc.2026.110323_b0070","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1136\/hrt.44.4.444","article-title":"Echocardiographic and anatomical correlates in the fetus","volume":"44","author":"Allan","year":"1980","journal-title":"Heart"},{"key":"10.1016\/j.bspc.2026.110323_b0075","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":"10.1016\/j.bspc.2026.110323_b0080","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1002\/uog.26224","article-title":"ISUOG practice guidelines (updated): Fetal cardiac screening","volume":"61","author":"Carvalho","year":"2023","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10.1016\/j.bspc.2026.110323_b0085","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/0735-1097(94)90429-4","article-title":"Antenatal diagnosis of coarctation of the aorta: a multicenter experience","volume":"23","author":"Hornberger","year":"1994","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.bspc.2026.110323_b0090","series-title":"Doppler Echocardiogr. Infancy Child","first-page":"297","article-title":"Interruption of the aortic arch","author":"Hofbeck","year":"2017"},{"key":"10.1016\/j.bspc.2026.110323_b0095","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.echo.2010.03.019","article-title":"Recommendations for quantification methods during the performance of a pediatric echocardiogram: a report from the pediatric measurements writing group of the American Society of Echocardiography Pediatric and Congenital Heart Disease Council","volume":"23","author":"Lopez","year":"2010","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"10.1016\/j.bspc.2026.110323_b0100","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s00404-023-06951-8","article-title":"How to do a fetal cardiac scan","volume":"307","author":"Quaresima","year":"2023","journal-title":"Arch. Gynecol. Obstet."},{"key":"10.1016\/j.bspc.2026.110323_b0105","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ejmp.2021.04.016","article-title":"Artificial intelligence and machine learning for medical imaging: a technology review","volume":"83","author":"Barrag\u00e1n-Montero","year":"2021","journal-title":"Phys. Med."},{"key":"10.1016\/j.bspc.2026.110323_b0110","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"},{"key":"10.1016\/j.bspc.2026.110323_b0115","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.bspc.2026.110323_b0120","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"10.1016\/j.bspc.2026.110323_b0125","doi-asserted-by":"crossref","first-page":"3752","DOI":"10.1002\/mp.12350","article-title":"An adaptive fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images","volume":"44","author":"Feng","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110323_b0130","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.bspc.2026.110323_b0135","doi-asserted-by":"crossref","first-page":"3808","DOI":"10.3390\/app13063808","article-title":"Artificial intelligence approach for early detection of brain tumors using MRI images","volume":"13","author":"Aleid","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2026.110323_b0140","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2026.110323_b0145","series-title":"Med","first-page":"1623","article-title":"MedSegDiff: Medical image segmentation with diffusion probabilistic model","author":"Wu","year":"2023"},{"key":"10.1016\/j.bspc.2026.110323_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102953","article-title":"A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images","volume":"90","author":"Xu","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110323_b0155","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.3390\/biom10111526","article-title":"Image segmentation of the ventricular septum in fetal cardiac ultrasound videos based on deep learning using time-series information","volume":"10","author":"Dozen","year":"2020","journal-title":"Biomolecules"},{"key":"10.1016\/j.bspc.2026.110323_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2022.101150","article-title":"Deep learning-based real time detection for cardiac objects with fetal ultrasound video","volume":"36","author":"Iriani Sapitri","year":"2023","journal-title":"Inform. Med. Unlocked"},{"key":"10.1016\/j.bspc.2026.110323_b0165","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1002\/mp.13940","article-title":"Cardiac substructure segmentation with deep learning for improved cardiac sparing","volume":"47","author":"Morris","year":"2020","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110323_b0170","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s13246-023-01231-w","article-title":"Open-source, fully-automated hybrid cardiac substructure segmentation: Development and optimisation","volume":"46","author":"Finnegan","year":"2023","journal-title":"Phys. Eng. Sci. Med."},{"key":"10.1016\/j.bspc.2026.110323_b0175","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065724500540","article-title":"Automated quality assessment of medical images in echocardiography using neural networks with adaptive ranking and structure-aware learning","volume":"34","author":"Luosang","year":"2024","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.bspc.2026.110323_b0180","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TMI.2019.2930338","article-title":"Comparison of objective image quality metrics to expert radiologists\u2019 scoring of diagnostic quality of MR images","volume":"39","author":"Mason","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110323_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101897","article-title":"Automatic MR image quality evaluation using a deep CNN: a reference-free method to rate motion artifacts in neuroimaging","volume":"90","author":"Fantini","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.bspc.2026.110323_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102427","article-title":"Image quality assessment for machine learning tasks using meta-reinforcement learning","volume":"78","author":"Saeed","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110323_b0195","doi-asserted-by":"crossref","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":"10.1016\/j.bspc.2026.110323_b0200","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":"10.1016\/j.bspc.2026.110323_b0205","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.1016\/j.ultrasmedbio.2023.05.005","article-title":"Locating multiple standard planes in first-trimester ultrasound videos via the detection and scoring of key anatomical structures","volume":"49","author":"Zhen","year":"2023","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.bspc.2026.110323_b0210","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":"10.1016\/j.bspc.2026.110323_b0215","first-page":"30","article-title":"Diagnosing coronary heart disease using ensemble machine learning","volume":"7","author":"Miao","year":"2016","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.bspc.2026.110323_b0220","first-page":"1","article-title":"Early detection of coronary heart disease based on machine learning methods","volume":"4","author":"Yilmaz","year":"2022","journal-title":"Med. Rec."},{"key":"10.1016\/j.bspc.2026.110323_b0225","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.cmpb.2018.08.014","article-title":"A novel, data-driven conceptualization for critical left heart obstruction","volume":"165","author":"Meza","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110323_b0230","doi-asserted-by":"crossref","first-page":"14502","DOI":"10.1117\/1.JMI.4.1.014502","article-title":"Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms","volume":"4","author":"Pereira","year":"2017","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110323_b0235","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1002\/uog.27503","article-title":"Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection","volume":"63","author":"Athalye","year":"2024","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10.1016\/j.bspc.2026.110323_b0240","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ijcard.2021.12.012","article-title":"Deep learning-based computer-aided heart sound analysis in children with left-to-right shunt congenital heart disease","volume":"348","author":"Liu","year":"2022","journal-title":"Int. J. Cardiol."},{"key":"10.1016\/j.bspc.2026.110323_b0245","doi-asserted-by":"crossref","DOI":"10.1136\/wjps-2023-000580","article-title":"A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography","volume":"6","author":"Jiang","year":"2023","journal-title":"World J. Pediatr. Surg."},{"key":"10.1016\/j.bspc.2026.110323_b0250","doi-asserted-by":"crossref","first-page":"6454","DOI":"10.3390\/jcm11216454","article-title":"Deep learning for improving the effectiveness of routine prenatal screening for major congenital heart diseases","volume":"11","author":"Nurmaini","year":"2022","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.bspc.2026.110323_b0255","doi-asserted-by":"crossref","unstructured":"A. Tariq, A. Gill, H.K. Hussain, N. Jiwani, J. Logeshwaran, The smart earlier prediction of conginental heart disease in pregnancy using deep learning model, in: Proc. 2023 IEEE Technol. Eng. Manag. Conf. - Asia Pac., IEEE, Bengaluru, India, 2023: pp. 1\u20137. https:\/\/doi.org\/10.1109\/TEMSCON-ASPAC59527.2023.10531366.","DOI":"10.1109\/TEMSCON-ASPAC59527.2023.10531366"},{"key":"10.1016\/j.bspc.2026.110323_b0260","series-title":"In: 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT)","first-page":"140","article-title":"ECG heartbeat classification using ensemble of efficient machine learning approaches on imbalanced datasets","author":"Ahamed","year":"2020"},{"issue":"4","key":"10.1016\/j.bspc.2026.110323_b0265","doi-asserted-by":"crossref","first-page":"561","DOI":"10.3390\/medicina61040561","article-title":"The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: a Mini-Review","volume":"61","author":"Suha","year":"2025","journal-title":"Medicina"},{"key":"10.1016\/j.bspc.2026.110323_b0270","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1093\/oxfordjournals.rpd.a033149","article-title":"Mnsson, Methods for the evaluation of image quality: a review","volume":"90","author":"L.g.","year":"2000","journal-title":"Radiat. Prot. Dosimetry"},{"key":"10.1016\/j.bspc.2026.110323_b0275","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1007\/s10554-023-02941-8","article-title":"Evaluation of an artificial intelligence-based system for echocardiographic estimation of right atrial pressure","volume":"39","author":"Zamzmi","year":"2023","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"10.1016\/j.bspc.2026.110323_b0280","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.11.5.054002","article-title":"Automated echocardiography view classification and quality assessment with recognition of unknown views","volume":"11","author":"Jansen","year":"2024","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110323_b0285","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","article-title":"LabelMe: a database and web-based tool for image annotation","volume":"77","author":"Russell","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.bspc.2026.110323_b0290","doi-asserted-by":"crossref","unstructured":"Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, Q. Hu, ECA-Net: Efficient channel attention for deep convolutional neural networks, in: Proc. 2020 IEEECVF Conf. Comput. Vis. Pattern Recognit., IEEE, Seattle, WA, USA, 2020: pp. 11531\u201311539. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"10.1016\/j.bspc.2026.110323_b0295","unstructured":"G. Jocher, YOLOv5 by Ultralytics, (2020). https:\/\/doi.org\/10.5281\/zenodo.3908559."},{"key":"10.1016\/j.bspc.2026.110323_b0300","unstructured":"G. Jocher, YOLOv8 by Ultralytics, (2023). https:\/\/github.com\/ultralytics\/\/ultralytics (accessed February 15, 2025)."},{"key":"10.1016\/j.bspc.2026.110323_b0305","doi-asserted-by":"crossref","unstructured":"A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, G. Ding, YOLOv10: Real-time end-to-end object detection, in: Proc. 38th Int. Conf. Neural Inf. Process. Syst., Curran Associates Inc., Red Hook, NY, USA, 2024: pp. 107984\u2013108011.","DOI":"10.52202\/079017-3429"},{"key":"10.1016\/j.bspc.2026.110323_b0310","doi-asserted-by":"crossref","first-page":"110375","DOI":"10.1109\/ACCESS.2023.3316719","article-title":"Deep learning technique for congenital heart disease detection using stacking-based CNN-LSTM models from fetal echocardiogram: a pilot study","volume":"11","author":"Rahman","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110323_b0315","unstructured":"Arnaout, Rima, et al. \u201cDeep-learning models improve on community-level diagnosis for common congenital heart disease lesions.\u201darXiv preprint arXiv:1809.06993(2018)."},{"issue":"4","key":"10.1016\/j.bspc.2026.110323_b0320","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.18280\/ts.410438","article-title":"Fetal Heart Abnormality Detection in prior stage using LeNet 20 Deep Learning Architecture","volume":"41","author":"Patel","year":"2024","journal-title":"Traitement Du Signal"},{"issue":"3","key":"10.1016\/j.bspc.2026.110323_b0325","doi-asserted-by":"crossref","first-page":"4057","DOI":"10.1007\/s11227-021-04008-8","article-title":"An efficient multilayer deep detection perceptron (MLDDP) methodology for detecting testicular anomalies with or without congenital heart disease (TACHD)","volume":"78","author":"Kavitha","year":"2022","journal-title":"J. Supercomput."},{"key":"10.1016\/j.bspc.2026.110323_b0330","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."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426008773?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426008773?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:50:19Z","timestamp":1777593019000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426008773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":66,"alternative-id":["S1746809426008773"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110323","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AortaQA: Fetal congenital heart disease screening system based on the anatomically-Guided Dual-Quality control","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110323","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110323"}}