Data for AI in Congenital Heart Defects: Systematic Review.

Journal: Studies in health technology and informatics
PMID:

Abstract

Congenital heart disease (CHD) represents a significant challenge in prenatal care due to low prenatal detection rates. Artificial Intelligence (AI) offers promising avenues for precise CHD prediction. In this study we conducted a systematic review according to the PRISMA guidelines, investigating the landscape of AI applications in prenatal CHD detection. Through searches on PubMed, Embase, and Web of Science, 621 articles were screened, yielding 28 relevant studies for analysis. Deep Learning (DL) emerged as the predominant AI approach. Data types were limited to ultrasound and MRI sequences mainly. This comprehensive analysis provides valuable insights for future research and clinical practice in CHD detection using AI applications.

Authors

  • Paula Josephine Mayer
    Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Rasim Atakan Poyraz
    Core Unit eHealth and Interoperability, BIH at Charité, Berlin, Germany.
  • Thimo Hölter
    Core Unit eHealth and Interoperability, BIH at Charité, Berlin, Germany.
  • Sylvia Thun
    Charité Universitätsmedizin, Berlin Institute of Health, Germany.
  • Carina Nina Vorisek
    Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.