{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T18:16:35Z","timestamp":1783448195980,"version":"3.55.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["(project number YG2025QNA07)"],"award-info":[{"award-number":["(project number YG2025QNA07)"]}]},{"name":"the Pudong New Area Science and Technology Development Fund","award":["(project number PKJ2025-Y04)"],"award-info":[{"award-number":["(project number PKJ2025-Y04)"]}]},{"name":"the Sanya Science and Technology Innovation Program","award":["(project number 2022KJCX41)"],"award-info":[{"award-number":["(project number 2022KJCX41)"]}]},{"name":"the Hainan Province Science and Technology Special Fund","award":["(project number ZDYF2024SHFZ059)"],"award-info":[{"award-number":["(project number ZDYF2024SHFZ059)"]}]},{"name":"Shenzhen Medical Research Special Project clinical multi-center study","award":["(Project No. C2405001)"],"award-info":[{"award-number":["(Project No. C2405001)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02100-0","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:36:48Z","timestamp":1783445808000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Center Online Generalization Algorithm with Unadversarial Consistency for Fetal Heart Ultrasound View Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4115-8440","authenticated-orcid":false,"given":"Yiman","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongtong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahe","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changzhao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"2100_CR1","doi-asserted-by":"crossref","unstructured":"Verdurmen K M J, Eijsvoogel N B, Lempersz C, et al. A systematic review of prenatal screening for congenital heart disease by fetal electrocardiography[J]. International Journal of Gynecology & Obstetrics, 2016, 135(2): 129-134.","DOI":"10.1016\/j.ijgo.2016.05.010"},{"key":"2100_CR2","doi-asserted-by":"crossref","unstructured":"Germanakis I, Sifakis S. The impact of fetal echocardiography on the prevalence of liveborn congenital heart disease[J]. Pediatric cardiology, 2006, 27: 465-472.","DOI":"10.1007\/s00246-006-1291-6"},{"key":"2100_CR3","doi-asserted-by":"crossref","unstructured":"Zimmerman M S, Smith A G C, Sable C A, et al. Global, regional, and national burden of congenital heart disease, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017[J]. The Lancet Child & Adolescent Health, 2020, 4(3): 185-200.","DOI":"10.1016\/S2352-4642(19)30402-X"},{"key":"2100_CR4","doi-asserted-by":"crossref","unstructured":"Thomas H, Diamond J, Vieco A, et al. Global atlas of cardiovascular disease[J]. Glob Heart, 2018, 13(3): 143-63.","DOI":"10.1016\/j.gheart.2018.09.511"},{"key":"2100_CR5","doi-asserted-by":"publisher","unstructured":"Yu X, Ma L, Wang H, et al. Deep learning-based differentiation of ventricular septal defect from tetralogy of Fallot in fetal echocardiography images[J]. Technology and Health Care, 2024 (Preprint): 1\u20138, https:\/\/doi.org\/10.3233\/THC-248040.","DOI":"10.3233\/THC-248040"},{"key":"2100_CR6","doi-asserted-by":"crossref","unstructured":"Liu Y, Han X, Liang T, et al. EDMAE: An efficient decoupled masked autoencoder for standard view identification in pediatric echocardiography[J]. Biomedical Signal Processing and Control, 2023, 86: 105280.","DOI":"10.1016\/j.bspc.2023.105280"},{"key":"2100_CR7","doi-asserted-by":"crossref","unstructured":"Zhao Q M, Liu F, Wu L, et al. Prevalence of congenital heart disease at live birth in China[J]. The Journal of pediatrics, 2019, 204: 53-58.","DOI":"10.1016\/j.jpeds.2018.08.040"},{"key":"2100_CR8","unstructured":"Douglas P S, Garcia M J, Haines D E, Lai W W, Manning W J, Patel A R, et al.A report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, American Society of Echocardiography, American Heart Association[J].J Am Soc Echocardiogr, 2011,24(3):229\u2013267."},{"key":"2100_CR9","doi-asserted-by":"crossref","unstructured":"Kanwal A, Sheikh A M, Saher T. Determining the factors causing delayed referral for fetal echocardiography at a tertiary care hospital[J]. Journal of the Saudi Heart Association, 2018, 30(3): 205-210.","DOI":"10.1016\/j.jsha.2017.12.001"},{"key":"2100_CR10","doi-asserted-by":"crossref","unstructured":"Z\u00fchlke L, Lawrenson J, Comitis G, et al. Congenital heart disease in low-and lower-middle\u2013income countries: current status and new opportunities[J]. Current cardiology reports, 2019, 21: 1-13.","DOI":"10.1007\/s11886-019-1248-z"},{"key":"2100_CR11","doi-asserted-by":"crossref","unstructured":"Crino J P, Finberg H J, Frieden F, et al. AIUM practice guideline for the performance of obstetric ultrasound examinations[J]. Journal of Ultrasound in Medicine, 2013, 32(6): 1083-1101.","DOI":"10.7863\/jum.2013.32.6.1083"},{"key":"2100_CR12","doi-asserted-by":"publisher","unstructured":"Carvalho J S, Allan L D, Chaoui R, et al. ISUOG Practice Guidelines (updated): sonographic screening examination of the fetal heart[J]. Ultrasound Obstet Gynecol, 2013, 41: 348\u2013359, https:\/\/doi.org\/10.1002\/uog.12403.","DOI":"10.1002\/uog.12403"},{"key":"2100_CR13","doi-asserted-by":"crossref","unstructured":"Rychik J, Ayres N, Cuneo B, et al. American Society of Echocardiography guidelines and standards for performance of the fetal echocardiogram[J]. Journal of the American Society of Echocardiography, 2004, 17(7): 803-810.","DOI":"10.1016\/j.echo.2004.04.011"},{"key":"2100_CR14","doi-asserted-by":"crossref","unstructured":"Burgos-Artizzu X P, Coronado-Guti\u00e9rrez D, Valenzuela-Alcaraz B, et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes[J]. Scientific Reports, 2020, 10(1): 10200.","DOI":"10.1038\/s41598-020-67076-5"},{"key":"2100_CR15","doi-asserted-by":"crossref","unstructured":"Wu L, Dong B, Liu X, et al. Standard echocardiographic view recognition in diagnosis of congenital heart defects in children using deep learning based on knowledge distillation[J]. Frontiers in Pediatrics, 2022, 9: 770182.","DOI":"10.3389\/fped.2021.770182"},{"key":"2100_CR16","doi-asserted-by":"crossref","unstructured":"Zhao G, Kong D, Xu X, et al. Deep learning-based classification of breast lesions using dynamic ultrasound video[J]. European Journal of Radiology, 2023, 165: 110885.","DOI":"10.1016\/j.ejrad.2023.110885"},{"key":"2100_CR17","doi-asserted-by":"crossref","unstructured":"Komatsu M, Sakai A, Komatsu R, et al. Detection of cardiac structural abnormalities in fetal ultrasound videos using deep learning[J]. Applied Sciences, 2021, 11(1): 371.","DOI":"10.3390\/app11010371"},{"key":"2100_CR18","doi-asserted-by":"crossref","unstructured":"Zhang R, Yi G, Pu S, et al. Deep learning based on ultrasound to differentiate pathologically proven atypical and typical medullary thyroid carcinoma from follicular thyroid adenoma[J]. European Journal of Radiology, 2022, 156: 110547.","DOI":"10.1016\/j.ejrad.2022.110547"},{"key":"2100_CR19","doi-asserted-by":"crossref","unstructured":"Vullings R. Fetal electrocardiography and deep learning for prenatal detection of congenital heart disease[C]\/\/2019 Computing in Cardiology (CinC). IEEE, 2019: Page 1-Page 4.","DOI":"10.22489\/CinC.2019.072"},{"key":"2100_CR20","doi-asserted-by":"crossref","unstructured":"Yang Y, Wu B, Wu H, et al. Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning[J]. Journal of Perinatal Medicine, 2023, 51(8): 1052-1058.","DOI":"10.1515\/jpm-2023-0041"},{"key":"2100_CR21","doi-asserted-by":"crossref","unstructured":"Nurmaini S, Rachmatullah M N, Sapitri A I, et al. Deep learning-based computer-aided fetal echocardiography: application to heart standard view segmentation for congenital heart defects detection[J]. Sensors, 2021, 21(23): 8007.","DOI":"10.3390\/s21238007"},{"key":"2100_CR22","doi-asserted-by":"crossref","unstructured":"Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, et al. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use[J]. JAMA cardiology, 2021, 6(6): 624-632.","DOI":"10.1001\/jamacardio.2021.0185"},{"key":"2100_CR23","doi-asserted-by":"crossref","unstructured":"Blaivas M, Adhikari S, Savitsky E A, Blaivas L N, Liu Y T. Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between POCUS experts and a deep learning algorithm[J]. Journal of the American College of Emergency Physicians Open, 2020, 1(5): 857-864.","DOI":"10.1002\/emp2.12206"},{"key":"2100_CR24","doi-asserted-by":"crossref","unstructured":"Nurmaini S, Partan R U, Bernolian N, et al. Deep learning for improving the effectiveness of routine prenatal screening for major congenital heart diseases[J]. Journal of clinical medicine, 2022, 11(21): 6454.","DOI":"10.3390\/jcm11216454"},{"key":"2100_CR25","doi-asserted-by":"crossref","unstructured":"Jiang X, Yu J, Ye J, et al. A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography[J]. World Journal of Pediatric Surgery, 2023, 6(3): e000580.","DOI":"10.1136\/wjps-2023-000580"},{"key":"2100_CR26","doi-asserted-by":"crossref","unstructured":"Holste G, Oikonomou E K, Mortazavi B J, et al. Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning[J]. Communications Medicine, 2024, 4(1): 133.","DOI":"10.1038\/s43856-024-00538-3"},{"key":"2100_CR27","doi-asserted-by":"crossref","unstructured":"Wang G, Li W, Zhou M, et al. 4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome[J]. CAAI Transactions on Intelligence Technology, 2024, 9(6): 1485-1499.","DOI":"10.1049\/cit2.12354"},{"key":"2100_CR28","doi-asserted-by":"crossref","unstructured":"Ferdowsi M, Goh C H, Liu H, et al. Clinical Application of Artificial Intelligence in the Diagnosis, Prediction, and Classification of Coronary Heart Disease[J]. Cardiovascular Innovations and Applications, 2025, 10(1): 976.","DOI":"10.15212\/CVIA.2025.0009"},{"key":"2100_CR29","doi-asserted-by":"crossref","unstructured":"Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms[J]. NPJ digital medicine, 2020, 3(1): 10.","DOI":"10.1038\/s41746-019-0216-8"},{"key":"2100_CR30","doi-asserted-by":"crossref","unstructured":"Zhu P, Li Z. Guideline-based learning for standard plane extraction in 3-D echocardiography[J]. Journal of Medical Imaging, 2018, 5(4): 044503-044503.","DOI":"10.1117\/1.JMI.5.4.044503"},{"key":"2100_CR31","doi-asserted-by":"crossref","unstructured":"Hemmsen M C, Lange T, Brandt A H, et al. A methodology for anatomic ultrasound image diagnostic quality assessment[J]. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2016, 64(1): 206-217.","DOI":"10.1109\/TUFFC.2016.2639071"},{"key":"2100_CR32","doi-asserted-by":"publisher","unstructured":"Van Woudenberg N, Liao Z, Abdi A H, et al. Quantitative echocardiography: real-time quality estimation and view classification implemented on a mobile android device[C]\/\/Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation: International Workshops, POCUS 2018, BIVPCS 2018, CuRIOUS 2018, and CPM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16\u201320, 2018, Proceedings. Springer International Publishing, 2018: 74\u201381, https:\/\/doi.org\/10.1007\/978-3-030-01045-4_9.","DOI":"10.1007\/978-3-030-01045-4_9"},{"key":"2100_CR33","doi-asserted-by":"crossref","unstructured":"Zhang J, Gajjala S, Agrawal P, Tison, G H, Hallock L A, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy[J]. Circulation, 2018, 138(16): 1623-1635.","DOI":"10.1161\/CIRCULATIONAHA.118.034338"},{"key":"2100_CR34","doi-asserted-by":"crossref","unstructured":"Qiao S, Pang S, Luo G, et al. FLDS: An intelligent feature learning detection system for visualizing medical images supporting fetal four-chamber views[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 26(10): 4814-4825.","DOI":"10.1109\/JBHI.2021.3091579"},{"key":"2100_CR35","doi-asserted-by":"crossref","unstructured":"Chen J, Yang M, Gao G. Semi-supervised dual-branch network for image classification[J]. Knowledge-Based Systems, 2020, 197: 105837.","DOI":"10.1016\/j.knosys.2020.105837"},{"key":"2100_CR36","unstructured":"Salman H, Ilyas A, Engstrom L, et al. Unadversarial examples: Designing objects for robust vision[J]. Advances in Neural Information Processing Systems, 2021, 34: 15270-15284."},{"key":"2100_CR37","unstructured":"Sensoy M, Kaplan L, Kandemir M. Evidential deep learning to quantify classification uncertainty[J]. Advances in neural information processing systems, 2018, 31."},{"key":"2100_CR38","unstructured":"Yang T, Zhou S, Wang Y, et al. Test-time batch normalization[J]. arXiv preprint\u00a0arXiv:2205.10210, 2022."},{"key":"2100_CR39","doi-asserted-by":"crossref","unstructured":"Tang Y, Wang S, Qu Y, et al. Consistency and adversarial semi-supervised learning for medical image segmentation[J]. Computers in Biology and Medicine, 2023, 161: 107018.","DOI":"10.1016\/j.compbiomed.2023.107018"},{"key":"2100_CR40","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"2100_CR41","unstructured":"Wang D, Shelhamer E, Liu S, et al. Tent: Fully test-time adaptation by entropy minimization[J]. arXiv preprint\u00a0arXiv:2006.10726, 2020."},{"key":"2100_CR42","doi-asserted-by":"crossref","unstructured":"Wang W, Zhong Z, Wang W, et al. Dynamically instance-guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2023: 24090\u201324099.","DOI":"10.1109\/CVPR52729.2023.02307"},{"key":"2100_CR43","doi-asserted-by":"crossref","unstructured":"Dong H, Konz N, Gu H, et al. Medical image segmentation with intent: Integrated entropy weighting for single image test-time adaptation[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2024: 5046\u20135055.","DOI":"10.1109\/CVPRW63382.2024.00511"},{"key":"2100_CR44","doi-asserted-by":"crossref","unstructured":"Zhou Y, Wu J, Liao W, et al. TEGDA: Test-Time Evaluation-Guided Dynamic Adaptation for Medical Image Segmentation[C]\/\/International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2025: 628\u2013637.","DOI":"10.1007\/978-3-032-04978-0_60"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02100-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02100-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02100-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:38:08Z","timestamp":1783445888000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02100-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,7]]},"references-count":44,"alternative-id":["2100"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02100-0","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,7]]},"assertion":[{"value":"26 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Study approval was obtained from the ethics committee of the Hainan Women and Children\u2019s Medical Center (Approval No. HNWCMC-2024\u2013175). The study was performed in accordance with the relevant guidelines and\/or regulations including the Declaration of Helsinki. Informed consent to participate was waived for this retrospective cohort study by the ethics committee of the Hainan Women and Children's Medical Center.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}