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
Cited by
Sorted by citations
A review on deep-learning algorithms for fetal ultrasound-image analysis
Fiorentino, M. C., Villani, F. P., Cosmo, M. D., Frontoni, E., & Moccia, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102629 · Cited by 224
“Survey papers specifically dealing with US fetal images include: [11], [12], [13], where segmentation and classification algorithms are covered; [14], [15] that survey methods for fetal cardiology images; [16], [17] that briefly summarize DL methods for fetal abnormality detection, and [18] that…”
BackgroundHow Do Machines Learn? Artificial Intelligence as a New Era in Medicine
Koteluk, O., Wartecki, A., Mazurek, S., Kołodziejczak, I., & Mackiewicz, A. A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11010032 · Cited by 129
“The latest technological advances in ML also improve the diagnosis of fetal acidemia or hypoxia based on CTG analysis [115].”
MethodFetal health classification from cardiotocographic data using machine learning
Mehbodniya, A., Lazer, A. J. P., Webber, J. L., Sharma, D. K., Santhosh, J., Kousalya, K., Singh, P., Rajan, R., Pandya, S., & Sengan, S. (2021) · Expert Systems · doi:10.1111/exsy.12899 · Cited by 117
Multiomics, artificial intelligence, and precision medicine in perinatology
Pammi, M., Aghaeepour, N., & Neu, J. (2022) · Pediatric Research · doi:10.1038/s41390-022-02181-x · Cited by 113
“6 7 – 9 In this review, we intend to discuss recent research integrating multiomics using machine learning (ML) tools in the fi eld of Perinatology.”
BackgroundMennickent, D., Rodriguez, A. F., Opazo, M. C., Riedel, C. A., Castro, É., Eriz-Salinas, A., Appel-Rubio, J., Aguayo, C. R., Damiano, A. E., Guzmán‐Gutiérrez, E., et al. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1130139 · Cited by 123
“It has been demonstrated to be useful in the diagnosis and monitoring of fetuses with a compromised cardiovascular system, which may be related to several fetal conditions, such as IUGR, twin-to-twin transfusion syndrome, and CHD (140, 141).”
Background
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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