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dc.contributor.author García Cañadilla, Patricia, 1985-
dc.contributor.author Sanchez Martinez, Sergio
dc.contributor.author Crispi Brillas, Fàtima
dc.contributor.author Bijnens, Bart
dc.date.accessioned 2020-03-23T09:32:44Z
dc.date.available 2020-03-23T09:32:44Z
dc.date.issued 2020
dc.identifier.citation Garcia-Cañadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine learning in fetal cardiology: what to expect. Fetal Diagn Ther. 2020 Jan 7. DOI: 10.1159/000505021
dc.identifier.issn 1015-3837
dc.identifier.uri http://hdl.handle.net/10230/43986
dc.description.abstract 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.
dc.description.sponsorship This project was partially funded by the “la Caixa” Foundation under grant agreement LCF/PR/GN14/10270005, the Instituto de Salud Carlos III (PI17/00675) integrados en el Plan Nacional de I+D+I y cofinanciados por el ISCIII-Subdirección General de Evaluación y el Fondo Europeo de Desarrollo Regional (FEDER) “Una manera de hacer Europa”, the Centro de Investigación Biomédica en Red de Enfermedades Raras (ERPR04G719/2016), the Cerebra Foundation for the Brain-Injured Child (Carmarthen, Wales, UK), and AGAUR 2017 SGR grant No. 1531.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Karger (S. Karger AG)
dc.relation.ispartof Fetal diagnosis and therapy. 2020 Jan 7
dc.rights © 2020 S. Karger AG, Basel http://dx.doi.org/10.1159/000505021 ‘This is the peer-reviewed but unedited manuscript version of the following article: Garcia-Cañadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine learning in fetal cardiology: what to expect. Fetal Diagn Ther. 2020 Jan 7. DOI: 10.1159/000505021. The final, published version is available at http://www.karger.com/?doi=10.1159/000505021
dc.title Machine learning in fetal cardiology: what to expect
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1159/000505021
dc.subject.keyword Artificial intelligence
dc.subject.keyword Decision support systems
dc.subject.keyword Deep learning
dc.subject.keyword Echocardiography
dc.subject.keyword Fetal cardiology
dc.subject.keyword Machine learning
dc.subject.keyword Obstetrics
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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