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A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images

Lu, Y., Li, K., Pu, B., Tan, Y., & Zhu, N. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3222356 · Cited by 24 · see citing works

DPC-MSGATNet: dual-path chain multi-scale gated axial-transformer network for four-chamber view segmentation in fetal echocardiography

Qiao, S.-B., Pang, S.-C., Luo, G., Sun, Y., Yin, W.-J., Pan, S.-L., & Lv, Z. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-00968-x · Cited by 21 · see citing works

Automatic Segmentation of 2-D Echocardiography Ultrasound Images by Means of Generative Adversarial Network

Fatima, N., Afrakhteh, S., Iacca, G., & Demi, L. (2024) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2024.3393026 · Cited by 21 · see citing works

Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart

Wu, H., Wu, B., Lai, F., Liu, P., Lyu, G., He, S., & Dai, J. (2023) · Computational and Mathematical Methods in Medicine · doi:10.1155/2023/5650378 · Cited by 20 · see citing works

A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography

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A primer on artificial intelligence for the paediatric cardiologist

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A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images

Yan, L., Ling, S., Mao, R., Xi, H., & Wang, F. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01230-2 · Cited by 21 · see citing works

Diagnosis of fetal total anomalous pulmonary venous connection based on the post-left atrium space ratio using artificial intelligence

Wang, X., Yang, T., Zhang, Y., Liu, X., Zhang, Y., Sun, L., Gu, X., Chen, Z., Guo, Y., Xue, C., et al. (2022) · Prenatal Diagnosis · doi:10.1002/pd.6220 · Cited by 17 · see citing works

HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images

Yuan, Y., Li, C., Zhang, K., Yang, H., & Zhang, J. (2022) · Diagnostics · doi:10.3390/diagnostics12112852 · Cited by 16 · see citing works

A novel artificial intelligence model for fetal facial profile marker measurement during the first trimester

Ji, C., Liu, K., Yang, X., Cao, Y. W., Cao, X., Pan, Q., Yang, Z., Sun, L., Yin, L., Deng, X., et al. (2023) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-023-06046-x · Cited by 16 · see citing works

An Enhanced Feature Extraction Network for Medical Image Segmentation

Gao, Y. J., Che, X., Xu, H., & Bie, M. (2023) · Applied Sciences · doi:10.3390/app13126977 · Cited by 15 · see citing works

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

Gudigar, A., Raghavendra, U., Samanth, J., Vasudeva, A., Ashwal, A. J., Nayak, K., Tan, R., Ciaccio, E. J., Ooi, C. P., Barua, P. D., et al. (2022) · Informatics · doi:10.3390/informatics9020034 · Cited by 15 · see citing works

An improved semantic segmentation with region proposal network for cardiac defect interpretation

Nurmaini, S., Tama, B. A., Rachmatullah, M. N., Darmawahyuni, A., Sapitri, A. I., Firdaus, F., & Tutuko, B. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07217-1 · Cited by 14 · see citing works

Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers

Islam, U., Ali, Y. A., Al‐Razgan, M., Ullah, H., Almaiah, M. A., Tariq, Z., & Wazir, K. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-06526-4 · Cited by 13 · see citing works

Automatic echocardiographic anomalies interpretation using a stacked residual-dense network model

Nurmaini, S., Sapitri, A. I., Tutuko, B., Rachmatullah, M. N., Rini, D. P., Darmawahyuni, A., Firdaus, F., Mandala, S., Nova, R., & Bernolian, N. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05493-9 · Cited by 13 · see citing works

A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images

Ling, S., Yan, L., Mao, R., Li, J., Xi, H., Wang, F., Li, X., & He, M. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3390688 · Cited by 14 · see citing works

Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease

Liu, X., Zhang, Y., Zhu, H., Jia, B., Wang, J., He, Y., & Zhang, H. (2024) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2024.1345761 · Cited by 14 · see citing works

Artificial Intelligence in Fetal and Pediatric Echocardiography

Wang, A., Doan, T. T., Reddy, C. D., & Jone, P. (2024) · Children · doi:10.3390/children12010014 · Cited by 15 · see citing works

A comprehensive review on fetal health surveillance using ultrasound imagery

Mushtaq, G., & Veningston, K. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06644-8 · Cited by 14 · see citing works

A comprehensive scoping review on machine learning-based fetal echocardiography analysis

Hernandez-Cruz, N., Patey, O. V., Teng, C., Papageorghiou, A. T., & Noble, J. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109666 · Cited by 12 · see citing works

CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning

Elmekki, H., Alagha, A., Sami, H., Spilkin, A., Zanuttini, A., Zakeri, E., Bentahar, J., Kadem, L., Xie, W., Pîbarot, P., et al. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110003 · Cited by 11 · see citing works

Artificial intelligence in fetal echocardiography: Recent advances and future prospects

Ma, M., Sun, L., Chen, R., Zhu, J., & Zhao, B. (2024) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2024.101380 · Cited by 13 · see citing works

Use of Artificial Intelligence–Based Software to Aid in the Identification of Ultrasound Findings Associated With Fetal Congenital Heart Defects

Lam‐Rachlin, J., Punn, R., Behera, S. K., Geiger, M. K., Lachaud, M., David, N., Garmel, S. H., Fox, N. S., Rebarber, A., DeVore, G. R., et al. (2025) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000006087 · Cited by 9 · see citing works

Artificial intelligence, radiomics and fetal ultrasound: review of literature and future perspectives

Bouachba, A., Neves, J. D. J., Royer, E., Bartin, R., Salomon, L. J., Grévent, D., & Gorincour, G. (2025) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.29172 · Cited by 8 · see citing works

Simultaneous Segmentation of Four Cardiac Chambers in Fetal Echocardiography

An, S., Zhou, X., Zhu, H., Zhou, F., Wu, Y., Yang, T., Liu, X., Zhang, Y., Jiao, Z., & He, Y. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629908 · Cited by 7 · see citing works

AI-enabled workflow for automated classification and analysis of feto-placental Doppler images

Aguado, A. M., Jiménez-Pérez, G., Chowdhury, D., Prats-Valero, J., Martínez, S. S., Hoodbhoy, Z. A., Mohsin, S. S., Castellani, R., Testa, L., Crispi, F., et al. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1455767 · Cited by 6 · see citing works

Computer-aided assessment for enlarged fetal heart with deep learning model

Nurmaini, S., Sapitri, A. I., Roseno, M. T., Rachmatullah, M. N., Mirani, P., Bernolian, N., Darmawahyuni, A., Tutuko, B., Firdaus, F., Islami, A., et al. (2025) · iScience · doi:10.1016/j.isci.2025.112288 · Cited by 6 · see citing works

Segmentation of ten fetal heart components with coarse‐to‐fine cascading and dynamic feature powering

Yang, T., Zhang, Y., Zhu, M., Wang, Y., An, S., Gu, X., Liu, X., Han, J., He, Y., & Zhu, H. (2022) · IET Image Processing · doi:10.1049/ipr2.12597 · Cited by 6 · see citing works

Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal EchocardiographyPreprint

Qiao, S., Pang, S., Luo, G., Pan, S., Wang, X., Wang, M., Zhai, X., & Chen, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13096 · Cited by 6 · see citing works

A fast technique for hyper-echoic region separation from brain ultrasound images using patch based thresholding and cubic B-spline based contour smoothing

Chel, H., Bora, P. K., & Ramchiary, K. K. (2020) · Ultrasonics · doi:10.1016/j.ultras.2020.106304 · Cited by 5 · see citing works

Fuzzy Attention U-Net Architecture Based Localization and YOLOv5 Detection for Fetal Cardiac Ultrasound Images

Satish, S., & Herald Anantha Rufus, N. (2023) · Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications · doi:10.58346/jowua.2024.i1.001 · Cited by 5 · see citing works

Automated Quality Assessment of Medical Images in Echocardiography Using Neural Networks with Adaptive Ranking and Structure-Aware Learning

Luosang, G., Wang, Z., Liu, J., Zeng, F., Zhang, Y., & Wang, J. (2024) · International Journal of Neural Systems · doi:10.1142/s0129065724500540 · Cited by 7 · see citing works

Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method

Hurtado, J., Sierra-Franco, C. A., Motta, T., & Raposo, A. B. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109188 · Cited by 5 · see citing works

Artificial Intelligence for the Prenatal Ultrasound Diagnosis of Congenital Heart Disease: A Narrative Review

Riva, A., Guerra, M., Gangi, S. D., Veronese, P., & Vida, V. L. (2024) · Clinical and Experimental Obstetrics & Gynecology · doi:10.31083/j.ceog5111244 · Cited by 6 · see citing works

Tunicate swarm-based grey wolf algorithm for fetal heart chamber segmentation and classification: a heuristic-based optimal feature selection concept

Nageswari, C. S., Kumar, M. N. V., Grace, N. V. A., & Thiyagarajan, J. B. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-221654 · Cited by 8 · see citing works

Künstliche Intelligenz in der pränatalen kardialen Diagnostik

Weichert, J., Welp, A., Scharf, J. L., Dracopoulos, C., Rody, A., & Gembicki, M. (2021) · Der Gynäkologe · doi:10.1007/s00129-021-04890-6 · Cited by 4 · see citing works

A Disentanglement and Fusion Data Augmentation Approach for Echocardiography Segmentation

Monkam, P., Jin, S., Tang, B., Zhou, X., & Lu, W. (2022) · IEEE International Ultrasonics Symposium · doi:10.1109/ius54386.2022.9958234 · Cited by 4 · see citing works

Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentationPreprint

Jiménez-Pérez, G., Acosta, J., Alcaine, A., & Cámara, Ó. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.12996 · Cited by 4 · see citing works

EvoThy-Net: an evolutionary encoder-decoder network for thyroid nodule segmentation in ultrasound imaging

Sujini, G. N., & Balakrishna, S. (2026) · Scientific Reports · doi:10.1038/s41598-025-34731-8 · Cited by 3 · see citing works

Proposing novel methods for simultaneous cardiac cycle phase identification and estimating maximal and minimal left atrial volume (LAV) from apical four-chamber view in 2-D echocardiography

Barzegar, N., Khatibi, T., & Hosseinsabet, A. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100517 · Cited by 3 · see citing works

Artificial intelligence in congenital heart disease

Toscano, A., & Moras, P. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00042-1 · Cited by 3 · see citing works

Investigation on ultrasound images for detection of fetal congenital heart defects

Satish, S., & Rufus, N. H. A. (2024) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad4f91 · Cited by 4 · see citing works

A Novel Conv-Mamba-Hybrid Network for Medical Image Segmentation

Qiao, S., Zhao, Z., Xie, P., Yin, W., Wang, M., Zhang, Y., & Pang, S. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821714 · Cited by 2 · see citing works

Thoughts on the contribution of artificial intelligence ( AI ) to assessment of the fetal heart: a true scientific odyssey

Quarello, E., & Corno, E. (2025) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.29185 · Cited by 2 · see citing works

Machine learning-based method for the detection of dextrocardia in ultrasound video clips

Hernandez-Cruz, N., Patey, O. V., Šalović, B., Papageorghiou, A. T., & Noble, J. A. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109023 · Cited by 1 · see citing works

Possibilities of applying machine learning methods to improve the quality of prenatal diagnosis of congenital malformations: scoping review

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

Automated interpretation of fetal cardiac function evaluation from the echocardiogram

Huang, C., Zhang, L., Xie, B., Jiang, Y., Zhu, Y., Liu, X., Lei, T., He, M., Yan, Y., Wang, N., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02381-3 · Cited by 1 · see citing works

Artificial intelligence in pediatric congenital and acquired heart disease

Rangu, S., & Reddy, C. D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00038-3 · Cited by 1 · see citing works

A Fast Level-Set Curve-Flow Formulation by Logarithmic Step-Size Control for Brain Ultrasound Image Segmentation

Chel, H., Bora, P. K., & Ramchiary, K. K. (2023) · IEEE Access · doi:10.1109/access.2023.3335045 · Cited by 1 · see citing works

Identification of Congenital Heart Defects in Ultrasound Images using U-Net Segmentation

Pandey, S. K., Diwan, R., Rose, A., Singh, K. U., Singh, T., & Somasekar, J. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489015 · Cited by 1 · see citing works

A multi-level contrastive learning-based correction model for semi-supervised fetal echocardiography segmentation

Yu, Q., Song, D., Yang, J., Zhang, Z., Sun, S., Sun, H., Li, Y., Liu, Y., Qi, Y., Wei, M., et al. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130858 · Cited by 1 · see citing works

ARTIFICIAL INTELLIGENCE-BASED FETAL CONGENITAL HEART DISEASE DIAGNOSTICS: A SURVEY OF STATE-OF-THE-ART TECHNIQUES

ZHAO, Y., TANG, W., Fan, S., Chen, X., & Ji, X. (2023) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519423400833

FHD deep learning prognosis approach: Early detection of fetal heart disease (FHD) using ultrasonography image-based IROI combined multiresolution DCNN

Someshwaran, G., & Sarada, V. (2025) · Technology and Health Care · doi:10.1177/09287329241310981

Artificial Intelligence-Assisted Echocardiographic Image-Analysis for the Diagnosis of Fetal Congenital Heart Disease: A Systematic Review and Meta-Analysis

Gan, Y., Yang, L., & Liao, J. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm28060 · Cited by 1 · see citing works

Applications of Artificial Intelligence on Fetal Echocardiography

Alves, J. A., Melo, M. M., Teixeira, L. M., Bravo‐Valenzuela, N. J., & Júnior, E. A. (2025) · Congenital Heart Disease · doi:10.32604/chd.2025.066358

Cardio Thoracic Ratio for Congenital Heart Disease detection using Deep Learning

Venugopal, S. T., Sriraam, N., & Sheshadri, S. (2025) · International Conference on Biomedical Engineering and Sustainable Healthcare (ICBMESH) · doi:10.1109/icbmesh66209.2025.11182212

CSP-SAM: CNN-Enhanced and Self-prompting SAM for Ultrasound Anatomical Structure Segmentation

Yin, C., Dong, X., Tan, Y., Liang, B., Pu, B., & Jin, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5634-2_7

Diff-MOCS: A Diffusion Model-Based with Multi-Object Cardiac Ultrasound Images Segmentation

Ye, S., Zhang, Y., Meng, X., Li, C., & Sun, W. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356278

MSPD-SAM: A Prompt-Free Framework for Cardiac Segmentation Using Multi-scale Adapters and Parallel Decoding

Yin, C., Wang, L., Dong, X., & Jin, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6123-0_38

Artificial Intelligence in Pediatric and Fetal Echocardiography: Looking Beyond the Now

Gearhart, A. S., Perez-Kersey, P. P.-K., Barnes, T., Chang, A. C., Jone, P., & Lai, W. W. (2026) · Current Treatment Options in Pediatrics · doi:10.1007/s40746-026-00373-9

Convolutional-Neural-Network-Based Approach for Segmentation of Apical Four-Chamber View from Fetal Echocardiography

Xu, L., Liu, M., Zhang, J., & He, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2984630 · Cited by 50 · see citing works

DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View

Li, D., Guo, H., Chen, M., Chen, H., Chen, J., Liang, B., Liang, P., & Tan, Y. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-3480-7_31 · Cited by 2 · see citing works

DPC-MSGATNet: dual-path chain multi-scale gated axial-transformer network for four-chamber view segmentation in fetal echocardiography

Qiao, S.-B., Pang, S.-C., Luo, G., Sun, Y., Yin, W.-J., Pan, S.-L., & Lv, Z. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-00968-x · Cited by 21 · see citing works

Multiview and multiclass image segmentation using deep learning in fetal echocardiography

Wong, K. C. L., Sinkovskaya, E. S., Abuhamad, A. Z., & Syeda-Mahmood, T. (2021) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2582191 · Cited by 6 · see citing works

MobileUNet-FPN: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber Segmentation in Edge Computing Environments

Pu, B., Lu, Y., Chen, J., Li, S., Zhu, N., Wei, W., & Li, K. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3182722 · Cited by 87 · see citing works