Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester from Nuchal Ultrasonographic Images
Open accessAbstract
Importance: Accurate screening of trisomy 21 in the first trimester can provide an early opportunity for decision-making regarding reproductive choices. Objective: To develop and validate a deep learning model for screening fetuses with trisomy 21 based on ultrasonographic images. Design, Setting, and Participants: This diagnostic study used data from all available cases and controls enrolled at 2 hospitals in China between January 2009 and September 2020. Two-dimensional images of the midsagittal plane of the fetal face in singleton pregnancies with gestational age more than 11 weeks and less than 14 weeks were examined. Observers were blinded to subjective fetus nuchal translucency (NT) marker measurements. A convolutional neural network was developed to construct a deep learning model. Data augmentation was applied to generate more data. Different groups were randomly selected as training and validation sets to assess the robustness of the deep learning model. The fetal NT was shown and measured. Each detection of trisomy 21 was confirmed by chorionic villus sampling or amniocentesis. Data were analyzed from March 1, 2021, to January 3, 2022. Main Outcomes and Measures: The primary outcome was detection of fetuses with trisomy 21. The receiver operating characteristic curve, metrics of accuracy, area under the curve (AUC), sensitivity, and specificity were used for model performance evaluation. Results: A total of 822 case and control participants (mean [SD] age, 31.9 [4.6] years) were enrolled in the study, including 550 participants (mean [SD] age, 31.7 [4.7] years) in the training set and 272 participants (mean [SD] age, 32.3 [4.7] years) in the validation set. The deep learning model showed good performance for trisomy 21 screening in the training (AUC, 0.98; 95% CI, 0.97-0.99) and validation (AUC, 0.95; 95% CI, 0.93-0.98) sets. The deep learning model had better detective performance for fetuses with trisomy 21 than the model with NT marker and maternal age (training: AUC, 0.82; 95% CI, 0.77-0.86; validation: AUC, 0.73; 95% CI, 0.66-0.80). Conclusions and Relevance: These findings suggest that this deep learning model accurately screened fetuses with trisomy 21, which indicates that the model is a potential tool to facilitate universal primary screening for trisomy 21.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Walker et al., Zhang et al., Sciortino et al., and Deng et al. introduced AI models for automated detection and measurement of NT.
“The research groups Walker et al. [132], Zhang et al. [133], Sciortino et al. [134], and Deng et al. [135] addressed the time consuming process of NT measurement by introducing AI models for its automated detection and measurement”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [133]
The Results describe Zhang et al.’s deep-learning model for trisomy 21 screening from ultrasonographic images, so the paper does introduce an AI model in this setting. But the Methods say trained sonographers measured NT, and the Discussion > Limitations proposes prediction of NT length as future work. The paper does not support the attributed claim that its model automated NT detection and measurement. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA limitation of first trimester AI models was small data sets in rare anomalies.
“small data sets in rare anomalies [132,133]”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [133]
The Discussion’s Limitations section says the sample size was limited for this first-trimester deep-learning screening model. But the study addresses trisomy 21 alone, and the Introduction calls it the most prevalent chromosomal anomaly; it does not establish that small datasets are a limitation across rare anomalies generally. Page numbers were not provided in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDetection of rare pathologies is limited due to missing training of pattern recognition models, as AI algorithms depend on large data sets.
“As AI algorithms are usually dependent on large data sets for training, the detection of rare pathologies is limited due to missing training of pattern recognition models [132,133].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [133]
The Discussion > Limitations section says the study’s limited sample size constrained its screening model and that the model was trained only for trisomy 21, with trisomies 18 and 13 left for future work. It does not establish the broader claim that AI generally depends on large datasets or that rare pathologies are difficult to detect because pattern-recognition models lack training. (LLM, conf=0.83)
Experimental — accuracy not yet measuredA limitation of first trimester AI models was missing real-time application for clinical applicability.
“and missing real-time application for clinical applicability [133,134,138].”
A potential benefit of first trimester AI models was reduced workload.
“and reduced workload [133,135,136,137].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · see citing works
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works
Path Aggregation Network for Instance Segmentation
Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00913 · Cited by 10,011 · see citing works
Artificial intelligence in cancer imaging: Clinical challenges and applications
Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21552 · Cited by 1,846 · see citing works
Cell-free DNA Comprises an In Vivo Nucleosome Footprint that Informs Its Tissues-Of-Origin
Snyder, M. W., Kircher, M., Hill, A. J., Daza, R. M., & Shendure, J. (2016) · Cell · doi:10.1016/j.cell.2015.11.050 · Cited by 1,462 · see citing works
IEEE Transactions on Biomedical Engineering
(2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.10 · Cited by 1,412 · see citing works
Cell-free DNA Analysis for Noninvasive Examination of Trisomy
Norton, M. E., Jacobsson, B., Swamy, G. K., Laurent, L. C., Ranzini, A. C., Brar, H., Tomlinson, M. W., Pereira, L., Spitz, J. L., Hollemon, D., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1407349 · Cited by 817 · see citing works
Feature Pyramid Networks for Object DetectionPreprint
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03144 · Cited by 645 · see citing works
<scp>ISUOG</scp> Practice Guidelines: performance of first‐trimester fetal ultrasound scan
(2013) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.12342 · Cited by 621 · see citing works
Wang, K., Lu, X., Zhou, H., Gao, Y., Zheng, J., Tong, M., Wu, C., Liu, C., Huang, L., Jiang, T., et al. (2018) · Gut · doi:10.1136/gutjnl-2018-316204 · Cited by 503 · see citing works
A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis
Wang, S., Zha, Y., Li, W., Wu, Q., Li, X., Niu, M., Wang, M., Qiu, X., Li, H., He, Y., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.00775-2020 · Cited by 493 · see citing works
Wang, S., Zhou, M., Liu, Z., Liu, Z., Gu, D., Zang, Y., Dong, D., Gevaert, O., & Tian, J. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.06.014 · Cited by 479 · see citing works
Wang, S., Shi, J., Ye, Z., Dong, D., Yu, D., Zhou, M., Liu, Y., Gevaert, O., Wang, K., Zhu, Y., et al. (2019) · European Respiratory Journal · doi:10.1183/13993003.00986-2018 · Cited by 437 · see citing works
Dong, D., Tang, L., Li, Z. Y., Fang, M. J., Gao, J. B., Shan, X. H., Ying, X. J., Sun, Y. S., Fu, J., Wang, X. X., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz001 · Cited by 419 · see citing works
Kagan, K. O., Wright, D. E., Baker, A., Sahota, D. S., & Nicolaides, K. H. (2008) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.5331 · Cited by 377 · see citing works
Path Aggregation Network for Instance SegmentationPreprint
Liu, S., Qi, L., Qin, H., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01534 · Cited by 352 · see citing works
Machine learning for medical ultrasound: status, methods, and future opportunities
Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1517-0 · Cited by 262 · see citing works
Learning Deep Features for Discriminative LocalizationPreprint
Zhou, B., Khosla, A., Lapedriza, À., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.04150 · Cited by 202 · see citing works
Kagan, K. O., Cicero, S., Staboulidou, I., Wright, D., & Nicolaides, K. H. (2009) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.6318 · Cited by 172 · see citing works
Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning
Jiang, Y., Liang, X., Wang, W., Chen, C., Yuan, Q., Zhang, X., Li, N., Chen, H., Yu, J., Xie, Y., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.32269 · Cited by 120 · see citing works
Ciritsis, A., Rossi, C., Eberhard, M., Marcon, M., Becker, A. S., & Boss, A. (2019) · European Radiology · doi:10.1007/s00330-019-06118-7 · Cited by 117 · see citing works
Zhang, L., Dong, D., Zhang, W., Hao, X., Fang, M., Wang, S. Y., Li, W., Liu, Z., Wang, R., Zhou, J., et al. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.06.010 · Cited by 91 · see citing works
Wu, Q., Wang, S., Zhang, S., Wang, M., Ding, Y., Jin, F., Wu, Q., Qian, W., Liu, Z., Sun, K., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.11625 · Cited by 87 · see citing works
Ultrasound in Obstetrics and Gynecology
Grant, T. H., & Katzen, H. (2006) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/(issn)1469-0705 · Cited by 71 · see citing works
Zhou, H., Wang, K., & Tian, J. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2971065 · Cited by 71 · see citing works
Valetto, A., Orsini, A. A., Bertini, V., Toschi, B., Bonuccelli, A., Simi, F., Sammartino, I., Taddeucci, G., Simi, P., & Saggese, G. (2012) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2012.03.011 · Cited by 68 · see citing works
Zhong, L., Fang, X., Dong, D., Peng, H., Fang, M., Huang, C., He, B., Lin, L. C., Ma, J., Tang, L., et al. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.06.050 · Cited by 56 · see citing works
Abele, H., Wagner, P., Sonek, J., Hoopmann, M., Brucker, S. Y., Ülkümen, B. A., & Kagan, K. O. (2015) · Prenatal Diagnosis · doi:10.1002/pd.4664 · Cited by 48 · see citing works
Application of an individualized nomogram in first‐trimester screening for trisomy 21
Sun, Y., Zhang, L., Dong, D., Li, X., Wang, J., Yin, C., Poon, L. C., Tian, J., & Wu, Q. (2020) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.22087 · Cited by 32 · see citing works
Prefrontal space ratio in second‐ and third‐trimester screening for trisomy 21
Yazdi, B., Sonek, J., Oettling, C., Hoopmann, M., Abele, H., Schälike, M., & Kagan, K. O. (2012) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.12261 · Cited by 26 · see citing works
Zhang, L., Dong, D., Zhong, L., Li, C., Hu, C. Y., Yang, X., Liu, Z., Wang, R., Zhou, J., & Tian, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3087634 · Cited by 24 · see citing works
Kagan, K. O., Maier, V., Sonek, J., Abele, H., Lüthgens, K., Schmid, M., Wagner, P., & Hoopmann, M. (2018) · Fetal Diagnosis and Therapy · doi:10.1159/000489121 · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Rabbani, S. A., El‐Tanani, M., Sharma, S., Rabbani, S. S., El‐Tanani, Y., Kumar, R. R., & Saini, M. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5030037 · Cited by 102 · see citing works
Application and Progress of Artificial Intelligence in Fetal Ultrasound
Xiao, S., Zhang, J., Zhu, Y. C., Zhang, Z., Cao, H., Xie, M., & Zhang, L. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12093298 · Cited by 74 · see citing works
Shahrivar, R. Y., Karami, F., & Karami, E. (2023) · Biomimetics · doi:10.3390/biomimetics8070519 · Cited by 57 · see citing works
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
“The potential benefits of these models were the highly accurate non-invasive method for anomaly screening [134] and reduced workload [133,135–137].”
BackgroundMethodInfluentialShiwlani, A. (2024) · American Journal of Biomedical Science & Research · doi:10.34297/ajbsr.2024.22.002970 · Cited by 20 · see citing works
Artificial intelligence for prenatal chromosome analysis
Boddupally, K., & Thuraka, E. R. (2023) · Clinica Chimica Acta · doi:10.1016/j.cca.2023.117669 · Cited by 18 · see citing works
Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans
Peng, Y., Luo, Y., Yan, J., Li, W., Liao, Y., Yan, L., Ling, H., & Long, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-55974-x · Cited by 17 · see citing works
Artificial Intelligence in Imaging in the First Trimester of Pregnancy: A Systematic Review
Umans, E., Dewilde, K., Williams, H., Deprest, J. A., & den Bosch, T. V. (2024) · Fetal Diagnosis and Therapy · doi:10.1159/000538243 · Cited by 11 · see citing works
Tadepalli, K., Das, A., Meena, T., & Roy, S. S. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108682 · Cited by 15 · see citing works
A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques
Shaikh, M. A., Al-Rawashdeh, H. S., & Sait, A. R. W. (2025) · Life · doi:10.3390/life15030390 · Cited by 11 · see citing works
Do, H. D., Allison, J. J., Nguyen, H. L., Phung, H. N., Tran, C. D., Giang, L. M., & Nguyen, T. T. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e34476 · Cited by 10 · see citing works
Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain
Enache, I.-A., Iovoaica-Rămescu, C., Ciobanu, Ș. G., Berbecaru, E. I. A., Vochin, A., Băluță, I. D., Istrate-Ofiţeru, A.-M., Comănescu, C. M., Nagy, R. D., & Iliescu, D. G. (2024) · Life · doi:10.3390/life14020166 · Cited by 12 · see citing works
Liu, L., Wang, T., Zhu, W., Zhang, H., Tian, H., Li, Y., Cai, W., & Yang, P. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-025-07863-y · Cited by 8 · see citing works
Advancements in machine learning and biomarker integration for prenatal Down syndrome screening
Danaei, M., Rashnavadi, H., Yeganegi, M., Dastgheib, S. A., Bahrami, R., Azizi, S., Jayervand, F., Masoudi, A., Shahbazi, A., Shiri, A., et al. (2025) · Journal of Turkish Society of Obstetric and Gynecology · doi:10.4274/tjod.galenos.2025.12689 · Cited by 7 · see citing works
Lin, H., Deng, X., & Song, D. (2025) · Journal of Robotic Surgery · doi:10.1007/s11701-025-02756-w · Cited by 6 · see citing works
Invasive Prenatal Diagnostics: A Cornerstone of Perinatal Management
Świetlicki, A., Gutaj, P., Iciek, R., Awdi, K., Paluszkiewicz-Kwarcińska, A., & Wender-Ożegowska, E. (2024) · Applied Sciences · doi:10.3390/app14166915 · Cited by 7 · see citing works
Al-Mahrami, N., Jabri, N. A., Sallam, A. A. W., Jahdhami, N. A., & Zadjali, F. (2025) · Genes · doi:10.3390/genes16060695 · Cited by 4 · see citing works
Chen, C., Wang, C.-A., Cyue, J.-H., Chen, C.-P., Sun, F.-J., Chen, Y., Wang, L., Chang, C.-W., Tsai, B.-Y., & Chen, K. (2025) · Taiwanese Journal of Obstetrics and Gynecology · doi:10.1016/j.tjog.2025.08.001 · Cited by 4 · see citing works
Deep Learning Model for Real-Time Nuchal Translucency Assessment at Prenatal US
Zhang, Y., Yang, X., Ji, C., Hu, X., Cao, Y., Chen, C., Sui, H., Li, B., Zhen, C., Huang, W., et al. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.240498 · Cited by 4 · see citing works
Vidhyasagar, B. S., Madamanchi, S. C., & Kalimuthu, S. (2024) · International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) · doi:10.1109/ic-etite58242.2024.10493220 · Cited by 3 · see citing works
Deep Learning for Nuchal Translucency–Based Screening for Fetal Trisomy 21
Wataganara, T. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.17869 · Cited by 3 · see citing works
Nathan, N. O., Bergholt, T., Sejling, C., Ersbøll, A. S., Ekelund, K., Gerds, T. A., Gam, C. M. B. F., Rode, L., & Hegaard, H. K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308018 · Cited by 2 · see citing works
Integrative review of intelligent nuchal translucency for genetic disorder
Pawar, S. S., & Nikose, M. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108597 · Cited by 2 · see citing works
A Review of AI-Based Approaches for Suspected Fetal Abnormalities
Ansari, N. J. I., Edinburg, M. M., & NIKITHA, N. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-61471-2_30 · Cited by 1 · see citing works
Pawar, S. S., Nikose, M., & Gupta, A. U. (2025) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt64827.2025.10968330 · Cited by 1 · see citing works
Mironov, D. S., Александрович, С. И., Александровна, У. А., & Александрович, П. В. (2025) · Vrach i informacionnye tehnologii · doi:10.25881/18110193_2025_3_22 · Cited by 1 · see citing works
Degala, S. K. B., Singh, M., Tewari, R. P., Kamra, P., Pandey, R., & Kasiviswanathan, U. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132017 · Cited by 1 · see citing works
Wang, J., Wang, J., Peng, Y., Hou, F., Zhang, X., & Jin, H. (2026) · Annals of Medicine · doi:10.1080/07853890.2026.2688297
“17 However, few studies have explored end-to-end deep learning models for FGR using both first-and second-trimester ultrasound combined with clinical data.”
Hajiahmadi, S., Adariani, A. R., Amini, E., & Rasti, S. (2023) · Journal of Research in Medical Sciences · doi:10.4103/jrms.jrms_808_22
Hajiahmadi, S., Zarean, E., Gazvini, S., & Talebi, V. (2024) · Journal of diagnostic medical sonography · doi:10.1177/87564793241226934
Artificial Intelligence: A New Paradigm in Fetal Medicine Research and Clinical Practice
Kumar, R. S., Krishnan, K., Garg, N., & Bhatt, R. K. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-84404-1_4
Dunne, J., Kumarasamy, C., Belay, D. G., Betrán, A. P., Gebremedhin, A. T., Mengistu, S., Nyadanu, S. D., Roy, A., Tessema, G. A., Tamrat, T., et al. (2025) · medRxiv · doi:10.1101/2025.07.04.25330464
Pai, S., Gunagi, A. D., Aneesh, M., Azad, S. K., & Basthikodi, M. (2025) · IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) · doi:10.1109/discover66922.2025.11258961
Emerging applications of artificial intelligence for obstetric ultrasound: A scoping review
Gupta, V., Santos, N., Ripa, S. D., & Walker, D. A. (2026) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.70789 · Cited by 2 · see citing works
Kashani, M., Naghavi, F., & Rostami, M. (2026) · Research Square · doi:10.21203/rs.3.rs-8521430/v1
Hybrid YOLO-CoAtNet Model for Intelligent Detection of Critical Fetal Parameters
Bhyratae, S. A., Pai, S., Gunagi, A. D., Aneesh, M., Azad, S. K., & Basthikod, M. (2025) · International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC) · doi:10.1109/cosmic67569.2025.11380851
Reassurance‐Focused First‐Trimester Euploidy Screening With Machine Learning
Szenejko, P. I., Dabrowski, F. A., Płotka, S., Sitek, A., Agria, A., Witkowska, J., & Wiechec, M. T. (2026) · Prenatal Diagnosis · doi:10.1002/pd.70151
Liu, L., Wang, T., Li, Y., Tian, H., Zhang, H., Zhou, C., Zhu, W., & Cai, W. (2026) · The Ultrasound Journal · doi:10.5826/tuj.2026.18334
Pawar, S. S., Nikose, M., & Gupta, A. U. (2026) · International Conference on Emerging Smart Computing and Informatics (ESCI) · doi:10.1109/esci68015.2026.11493192
A Structure-Aware Self-Supervised Framework for Transferable Fetal Ultrasound ScreeningPreprint
Zhu, Y., Wu, J., Gao, L., Zhang, N., Wu, L., Li, H., chen, K., Luo, H., Sun, C., Daru, L., et al. (2026) · medRxiv · doi:10.64898/2026.09.21.26361576
Kaboré, S., Bayani, A., Ewane, L. P. E., Anjos, D. S. O. D., Nikiema, J. N., & Mac-Seing, M. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001749
- Authors
- Zhang, L., Dong, D., Sun, Y., Hu, C., Sun, C., Wu, Q., & Tian, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Jinan University (CN)
- Capital Medical University (CN)
- Department of Ultrasound, Shijiazhuang Obstetrics and Gynecology Hospital, Shijiazhuang, China
- Corresponding author
- Wu, Qingqing
- Tian, Jie
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2022-06-21
- Volume
- 5
- Issue
- 6
- Pages
- e2217854
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC9214589
- OpenAlex
- W4283276009
- Cited by
- 42
- References
- 34
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 11 · 2023: 5 · 2022: 1
- FWCI
- 9.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (2017YFC1309100, 91959130, HLHPTP201703, 82022036, 91959130, 81971776, 81771924, 6202790004, 81930053, 6202790004, 81771924, 81930053, QYZDJ-SSW-JSC005, 81971776, 81771924, 81971776, 2017YFA0205200, 82022036)
- Chinese Academy of Sciences (QYZDJ - SSW - JSC005, 2017175, 2017YFC1309100, XDB 38040200, 2017YFA0205200, GJJSTD2017 0004, 81930053, 38040200, 81771924, JSC005, HLHPTP201703)
- Youth Innovation Promotion Association of the Chinese Academy of Sciences (2017175, QYZDJ-SSW-JSC005, 81930053, 2017YFA0205200)
- Youth Innovation Promotion Association (2017175)
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Down syndrome, Down syndrome screening, first trimester, nuchal translucency, convolutional neural networks, deep learning, prenatal diagnosis, maternal age, chorionic villus sampling, amniocentesis, model validation, data augmentation, singleton pregnancy
- MeSH terms
- Deep Learning, Adult, Down Syndrome, Female, Humans, Infant, Pregnancy, Pregnancy Trimester, First, Pregnancy-Associated Plasma Protein-A, Prenatal Diagnosis, Trisomy, Biomarkers, Chorionic Gonadotropin, beta Subunit, Human
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
