The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
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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Cites
Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466
Interactive medical image segmentation using deep learning with image-specific fine tuning
Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791721 · Cited by 843
Fetal growth restriction results in remodeled and less efficient hearts in children
Crispi, F., Bijnens, B., Figueras, F., Bartrons, J., Eixarch, E., Le Noble, F., Ahmed, A., & Gratacós, E. (2010) · Circulation · doi:10.1161/circulationaha.110.937995 · Cited by 435
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
Rajchl, M., Lee, M. C. H., Oktay, O., Kamnitsas, K., Passerat-Palmbach, J., Bai, W., Damodaram, M., Rutherford, M. A., Hajnal, J. V., Kainz, B., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2621185 · Cited by 410
Cardiac dysfunction and cell damage across clinical stages of severity in growth-restricted fetuses
Crispi, F., Hernandez‐Andrade, E. A., Pelsers, M. M. A. L., Plasencia, W., Benavides-Serralde, J. A., Eixarch, E., le Noble, F. A. C., Ahmed, A., Glatz, J. F. C., Nicolaides, K. H., et al. (2008) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2008.06.056 · Cited by 297
Čikeš, M., Sanchez‐Martinez, S., Claggett, B. L., Duchateau, N., Piella, G., Butakoff, C., Pouleur, A., Knappe, D., Biering‐Sørensen, T., Kutyifa, V., et al. (2018) · European Journal of Heart Failure · doi:10.1002/ejhf.1333 · Cited by 274
Rychik, J. R., Tian, Z., Bebbington, M. W., Xu, F., McCann, M., Mann, S., Wilson, R. D., & Johnson, M. P. (2007) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2007.06.055 · Cited by 265
FUIQA: Fetal Ultrasound Image Quality Assessment with Deep Convolutional Networks
Wu, L., Cheng, J.-Z., Li, S., Lei, B., Wang, T., & Ni, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2671898 · Cited by 242
Ultrasound‐based gestational‐age estimation in late pregnancy
Papageorghiou, A. T., Kemp, B., Stones, W., Ohuma, E. O., Kennedy, S. H., Purwar, M., Salomon, L. J., Altman, D. G., Noble, J. A., Bertino, E., et al. (2016) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.15894 · Cited by 179
Kuhle, S., Maguire, B. R., Zhang, H., Hamilton, D. C., Allen, A. C., Joseph, K. S., & Allen, V. M. (2018) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-018-1971-2 · Cited by 166
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A Systematic Review on The Applications of Machine Learning for Fetal Birth Weight PredictionPreprint
Mane, D., Mante, J., Bakare, A. A., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3440424/v1 · Cited by 1
Cost-Utility Analysis of Prenatal Diagnosis of Congenital Cardiac Diseases using Deep LearningPreprint
Ginsberg, G. M., Drukker, L., Pollak, U., & Brezis, M. (2024) · doi:10.22541/au.171045405.59757841/v1 · Cited by 1
Quarello, E., Regouin, M., Mancini, J., Lafouge, A., FONTAINE, N., Roussin, S., GUICHARD, J., & Dumont, C. (2021) · doi:10.22541/au.161554738.83611762/v1
Quantitative Assessment of Fetal Wellbeing Through CTG Recordings
Sharma, S. B., Ashu, A., Gupta, A., et al. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-97929-4_13
Bardi, F. (2022) · doi:10.33612/diss.245084236
Detecting Abnormalities of Fetal Cardiac Using Deep Learning in Parallel Computing Environment
Magesh, S., Rajakumar, P. S., Ananthan, T. V., et al. (2022) · Advances in parallel computing · doi:10.3233/apc220009
Review on Fetal Health Classification
Nagabotu, V., & Namburu, A. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1435-7_6
Fetal Cardiac Detection Using Deep Learning from Echocardiographic Image–A Survey
Pradeep, D., Prasath, S. D., Edwin, J. J., & Kumaravel, P. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3761-5_6
Automated Recognition of Congenital Heart Defects (CHD) from Ultrasonic Fetal Heart Biometry
Sushma, T. V., Sriraam, N., & Suresh, S. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2571/1/012015
“The use of machine learning tools in fetal echocardiography has[16] have presented a detailed review of the use of used Traditional approaches that involve extraction of man made features and techniques using deep learning have been discussed.”
MethodA Systematic Review on the Applications of Machine Learning for Fetal Birth Weight Prediction
Mane, D., Mante, J., Bakare, A. A., Gandhi, Y. B., Khetani, V., & Mahajan, R. A. (2024) · International Journal of Media and Networks · doi:10.33140/ijmn.02.02.04 · Cited by 3
Anais do Simpósio Brasileiro de Inteligência Artificial
Pontes, M. R., Damasceno, M. N., da Silva, G. L., & Mariquito, M. F. A. (2024) · Editora Pascal LTDA eBooks · doi:10.29327/5424868
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
Künstliche (Artifizielle) Intelligenz (KI oder AI) im Ultraschall
Weichert, J., & Kollmann, C. (2025) · Springer eBooks · doi:10.1007/978-3-662-67373-7_40
Gan, Y., Yang, L., & Liao, J. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm28060 · Cited by 1
“Studies have used AI in fetal echocardiography to improve the diagnostic accuracy of fetal CHD [23] and have shown that some AI models equal the performance level of experts [24,25].”
Fetal Intelligent Navigation Echocardiography (FINE)
Weichert, J., & Weichert, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-87175-7_17
A Survey on Fetal ECG Extraction Techniques in the Era of Machine Learning
Prachi, Sabherwal, P., Agrawal, M., et al. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7134-2_8
Rodríguez, S., Perez, A., Echeverry, L. M., Castillo, A., Ramírez, N. A., Escobar, M., Monroy, S. G., Vega, D., Rodríguez, N., Castro-Páez, C., et al. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1726484 · Cited by 1
Nguyen, P. T., & Sahara, M. (2026) · Stem Cell Research & Therapy · doi:10.1186/s13287-026-05015-3 · Cited by 1
“These metabolic changes may contribute to right ventricular hypertrophy and dysfunction during hemodynamic stress [55, 56].”
Congenital heart disease diagnosis using machine learning: a systematic literature review
Khan, K., Syed, I., Ullah, F., & Khan, R. U. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1757468 · Cited by 7
Ito, A., Nagayasu, Y., Mitsuhashi, R., et al. (2026) · Journal of obstetrics and gynaecology research · doi:10.1111/jog.70467
“However, systematic reviews conducted after 2018 concluded that AI-based FHR analysis did not improve neonatal acidemia, death, or intensive care unit admission rates [10, 11].”
Pardasani, R., Vitullo, R., Harris, S., et al. (2026) · Pregnancy · doi:10.1002/pmf2.70433
“These interpretations largely depend on clinicians’ expertise, and their subjective nature has led to significant inter-and intra-observer variability, posing challenges to consistent clinical decision-making [4].”
Li, Z., Du, M. L., & Li, F. (2026) · Advanced ultrasound in diagnosis and therapy · doi:10.26599/audt.2026.250063
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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