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HEART DISEASE

Deep learning for detecting congenital heart disease in the fetus

Nature Medicine volume 27, pages 764–765 (2021)Cite this article

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New advances in machine learning could facilitate and reduce disparities in the prenatal diagnosis of congenital health disease, the most common and lethal birth defect.

Congenital heart disease (CHD) is the most common birth defect, affecting ~1% of live births, and is the largest birth-defect-related contributor to infant mortality in developed countries1. Prenatal diagnosis of critical CHD allows for family preparation and decision-making, and also allows delivery planning for optimal neonatal intervention and medical care. Children with a prenatal diagnosis have been shown to have lower morbidity, lower mortality for some lesions, less preoperative brain injury and more-robust microstructural brain development than that of those with a postnatal diagnosis of CHD2,3.

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Fig. 1: Conflicting drivers of prenatal diagnosis of CHD, and the mechanism by which technological advances such as machine learning have the potential to both improve access but also possibly increase disparities.

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Authors and Affiliations

  1. Department of Pediatrics, Division of Pediatric Cardiology, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX, USA

    Shaine A. Morris & Keila N. Lopez

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  1. Shaine A. Morris
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  2. Keila N. Lopez
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Correspondence to Shaine A. Morris.

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The authors declare no competing interests.

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Morris, S.A., Lopez, K.N. Deep learning for detecting congenital heart disease in the fetus. Nat Med 27, 764–765 (2021). https://doi.org/10.1038/s41591-021-01354-1

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  • DOI: https://doi.org/10.1038/s41591-021-01354-1

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