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Machine Learning in Fetal Cardiology: What to Expect
Open accessAbstract
In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.
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“Furthermore, most previously reported works in the literature had been evaluated based on the intra-patient paradigm rather than the inter-patient scheme, which is a more realistic scenario to prevent training and test the model using samples from the same patients [21].”
From Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases (2022) · cites this paper as [21]
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A review on deep-learning algorithms for fetal ultrasound-image analysis
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BackgroundModel-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos
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Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring
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BackgroundMethodAuto-tuning Fixed-point Precision with TVM on RISC-V Packed SIMD Extension
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A deep learning framework for noninvasive fetal ECG signal extraction
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Accurately assessing congenital heart disease using artificial intelligence
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“Moreover, classic data augmentation methods already have good performance 86 .”
Artificial Intelligence in Clinical Toxicology
Sinha, M., Praveen, G. B., Sachan, D. K., & Parthasarathi, R. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_137-1 · Cited by 8
Lewandrowski, K., Abraham, I. L., León, J. F. R., Cantú-Leal, R., Longoria, R. C., Sánchez, J. A. S., & Yeung, A. T. (2022) · The International Journal of Spine Surgery · doi:10.14444/8200 · Cited by 8
“these observations are corroborated by 2 retrospective clinical cohort studies that evaluated clinical outcomes in patients who underwent combined transforaminal endoscopic decompression with simultaneous placement of an iPs.(33,158) another 2year followup study performed by this team of authors on simultaneous Table…”
ResultWhat Does Fetal Echocardiography Add Beyond the Anomaly Scan?
Simpson, J. M., & Hornberger, L. K. (2022) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.122.014168 · Cited by 6
Sriraam, N., Punyaprabha, V., Sushma, T. V., & Suresh, S. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02814-1 · Cited by 5
A Machine Learning–Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns
Deval, R., Saxena, P., Pradhan, D., Mishra, A. K., & Jain, A. K. (2022) · The Indian Journal of Pediatrics · doi:10.1007/s12098-022-04273-2 · Cited by 6
Prediction of Late Intrauterine Growth Restriction using Machine Learning Models
Magboo, V. P. C., & Magboo, M. S. A. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.199 · Cited by 5
Patel, R. P., Patel, M., Patel, Z., et al. (2024) · Global Academic Journal of Medical Sciences · doi:10.36348/gajms.2024.v06i01.008 · Cited by 5
Keeping the Ductus Arteriosus Patent: Current Strategy and Perspectives
Chatziantoniou, A., Rorris, F., Samanidis, G., & Kanakis, M. A. (2025) · Diagnostics · doi:10.3390/diagnostics15030241 · Cited by 4
“…congenital heart disease in fetal life, either by identification of reverse flow in the aortic arch or the ductus arteriosus or through a deep learning artificial intelligence software that processes acquired ultrasound images with results close to those of experienced sonographers [45–47].”
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
Lad, A., Narayan, A., Shankar, H., et al. (2022) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2610741 · Cited by 4
Machine Learning for Ischaemic Heart Disease Diagnostic Analysis
Xing, R., & Meng, J. (2022) · IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) · doi:10.1109/ecbios54627.2022.9944997 · Cited by 4
“ML techniques also have the potential to improve the diagnoses of foetal cardiac function and remodelling by optimising image acquisition and quantification [6].”
BackgroundBardi, F., Bakker, M., Elvan‐Taşpınar, A., Kenkhuis, M. J. A., Fridrichs, J., Bakker, M. K., Birnie, E., & Bilardo, C. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279770 · Cited by 5
Deep Learning Approach for Fetal Health Prediction
Suganthi, S. R. L., & Basilica, M. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit241051041 · Cited by 3
Prediction of Fetal Abnormalities at early stage with GradientBoosting and Polynomial Feature
Khan, A., Singh, A. K., Lohith Datta, M., Bagepalli, A. R., Dinesh, P., & Pokalkar, D. (2022) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt54827.2022.9984319 · Cited by 3
“[ 19][20][21] The sections for this article's description are shown below.”
MethodApplications of Text Generation in Digital Marketing: a review
Loukili, S., Fennan, A., & Elaachak, L. (2023) · International Conference on Networking, Information Systems & Security (NISS) · doi:10.1145/3607720.3608451 · Cited by 3
Sulistianingsih, N., & Martono, G. H. (2024) · Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer · doi:10.30812/matrik.v23i2.3788 · Cited by 3
Bansal, N., Pahuja, S., & Kaur, I. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1373-2_4 · Cited by 2
Vellvé, K., García‐Canadilla, P., Nogueira, M. A., Youssef, L., Arranz, Á., Nakaki, A., Boada, D., Blanco, I., Faner, R., Figueras, F., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-54603-x · Cited by 2
Ethical Considerations in Fetal Cardiology
Samples, S. M., Gandhi, R., Woo, J. L., & Patel, A. (2024) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd11060172 · Cited by 2
“Machine learning may also one day help improve the evaluation of fetal cardiac structures by optimizing image acquisition and quantification, thereby improving prenatal diagnosis capabilities [17].”
BackgroundImplementation of resource-efficient fetal echocardiography detection algorithms in edge computing
Zhu, Y., Gao, Y. D., Wang, M., Li, M., & Wang, K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305250 · Cited by 2
Francis, J., George, J., Peng, E. W. K., & Corno, A. F. (2024) · Regenerative medicine reports . · doi:10.4103/regenmed.regenmed-d-24-00013 · Cited by 2
Quarello, E., & Corno, E. (2025) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.29185 · Cited by 3
Artificial Intelligence in Clinical Toxicology
Sinha, M., Praveen, G. B., Sachan, D. K., & Parthasarathi, R. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_137 · Cited by 2
Congenital Heart Diseases in Pregnancy
Bernolian, N., Partan, R. U., Nurmaini, S., Kesty, C., & Widodo, B. W. (2021) · Bioscientia Medicina Journal of Biomedicine and Translational Research · doi:10.32539/bsm.v5i4.376 · Cited by 2
“Cited from: Garcia-Canadilla et al., 2020 Machine learning is capable of identifying fetal cardiac features, assisting segmentation and measurement of cardiac structures, and distinguishing normal cardiac shape from data pool, especially when used in a supervised learning format.36…”
MethodZhang, H., Wang, R., Zhou, H., Xia, S., Jia, S., & Wu, Y. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/3226655 · Cited by 2
Hardalaç, F., Akmal, H., Ayturan, K., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4197627 · Cited by 2
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
Erenbourg, A., Barber, T. J., Cecotti, V., Faiola, S., Fantasia, I., Stampaljia, T., Avnet, H., Radzymińska-Chruściel, B., Meriki, N., & Welsh, A. W. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-025-07145-7 · Cited by 1
“Automation of fetal functional cardiac measurements may be an important step towards standardisation and improved repeatability increasing their clinical utility [23–25].”
Optical Flow-Enhanced Mamba U-Net for Cardiac Phase Detection in Ultrasound Videos
Lu, Y., Tan, G., Pu, B., Yeung, P.-H., Wang, H., Li, S., Rajapakse, J. C., & Li, K. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3550731 · Cited by 1
Fetal Cardiac Anomaly Detection Models Using Pattern Classification and FetalEcho_V01 Dataset
O, D. M., Vijaya, M. S., & Ranjitha, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3372-2_30 · Cited by 1
Enhancing Fetal Cardiac Imaging With Artificial Intelligence
Martins, J. G., Horgan, R., & Sinkovskaya, E. S. (2025) · Clinical Obstetrics & Gynecology · doi:10.1097/grf.0000000000000992 · Cited by 1
AI ‐Driven Analysis of the Fetal Left Ventricular Outflow Tract
Turan, Ş., Velly, C., Bisanti, M., Mancini, J., Lafouge, A., Voznyuk, I., & Quarello, E. (2026) · Journal of Ultrasound in Medicine · doi:10.1002/jum.70230 · Cited by 1
Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study
Houri, O., Youssef, L., Crovetto, F., Borrell, M., Crimella, M., Ferrante, M. G., Novoa, R. H., Casas, I., Encabo, N., Benítez, L., et al. (2026) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.70262 · Cited by 1
Employing Support Vector Machine Regression to Estimate the Fetal Gestational Age
Wahbah, M., Sakaji, R. A., Funamoto, K., Krishnan, A. N., Kasahara, Y., Kimura, Y., & Khandoker, A. H. (2022) · Computing in cardiology · doi:10.22489/cinc.2022.170 · Cited by 1
“The Machine Learning (ML) framework [8] was employed in [9] to predict fetal GA based on ultrasound brain image appearance.”
MethodGiraldo, S. M., Torres-Valencia, N. A., Torres-Valencia, C. A., & Restrepo, H. F. (2023) · Journal of Clinical Ultrasound · doi:10.1002/jcu.23604 · Cited by 1
“18 In the fetal brain, segmentation techniques have been included in intelligent training algorithms to classify a brain as normal or abnormal in a 2D ultrasound image.”
MethodML-Based Interpretation of Cardiotocography Data: Current State and Future Research
Kadarina, T. M., Basari, B., & Gunawan, D. (2023) · International Conference of Computer Science and Information Technology (ICoSNIKOM) · doi:10.1109/icosnikom60230.2023.10364517 · Cited by 2
“By these means, machine learning emphasizes its transformative potential in healthcare by strengthening proactive medical treatments and improving diagnostic precision [4].”
BackgroundNarayana, T. G. R. S., & Nalini, N. (2024) · AIP conference proceedings · doi:10.1063/5.0203728 · Cited by 1
Improving New Zealand Health Outcomes with Artificial Intelligence
Sehgal, T., & Cherrington, M. (2021) · Scope Contemporary Research Topics (Health and Wellbeing) · doi:10.34074/scop.3006005 · Cited by 1
Metadata
- Authors
- Garcia-Canadilla, P., Sanchez-Martinez, S., Crispi, F., & Bijnens, B.
- Affiliations
- Hospital Sant Joan de Déu Barcelona (ES)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Venue
- Fetal Diagnosis and Therapy
- Publisher
- S. Karger AG
- Published
- 2020
- Volume
- 47
- Issue
- 5
- Pages
- 363-372
- ISSN
- 1015-3837 (ISSN-L)
- 1421-9964
- OpenAlex
- W2999570143
- Semantic Scholar
- d64088672203ab58de09bbf1df30a68b3ec1ec92
- Cited by
- 123
- References
- 69
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 20 · 2023: 27 · 2022: 28 · 2021: 18 · 2020: 4
- FWCI
- 1.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Fetal Diagnosis and Therapy (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion)
- Funders
- Cerebra
- Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona
- Agència de Gestió d'Ajuts Universitaris i de Recerca (2017SGR)
- Instituto de Salud Carlos III (LCF/PR/GN14/10270005, AGAUR 2017, PI17/00675, ERPR04G719/2016)
- European Regional Development Fund (LCF/PR/GN14/10270005, PI17/00675)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Congenital Heart Disease Studies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, fetal cardiology, fetal echocardiography, fetal cardiac function, fetal cardiac anomalies, prenatal diagnosis, image segmentation, tissue Doppler imaging, M-mode imaging
- MeSH terms
- Machine Learning, Echocardiography, Female, Fetal Heart, Heart Defects, Congenital, Humans, Pregnancy, Prenatal Diagnosis, Ultrasonography, Prenatal
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