García Cañadilla, Patricia, 1985-Sanchez Martinez, SergioCrispi Brillas, FàtimaBijnens, Bart2020-03-232020-03-232020Garcia-Cañadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine learning in fetal cardiology: what to expect. Fetal Diagn Ther. 2020 Jan 7. DOI: 10.1159/0005050211015-3837http://hdl.handle.net/10230/43986In 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.application/pdfeng© 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=10.1159/000505021info:eu-repo/semantics/articlehttp://dx.doi.org/10.1159/000505021Artificial intelligenceDecision support systemsDeep learningEchocardiographyFetal cardiologyMachine learningObstetricsinfo:eu-repo/semantics/openAccess