A comprehensive scoping review on machine learning-based fetal echocardiography analysis.

Journal: Computers in biology and medicine
PMID:

Abstract

Fetal echocardiography (ultrasound of the fetal heart) plays a vital role in identifying heart defects, allowing clinicians to establish prenatal and postnatal management plans. Machine learning-based methods are emerging to support the automation of fetal echocardiographic analysis; this review presents the findings from a literature review in this area. Searches were queried at leading indexing platforms ACM, IEEE Xplore, PubMed, Scopus, and Web of Science, including papers published until July 2023. In total, 343 papers were found, where 48 papers were selected to compose the detailed review. The reviewed literature presents research on neural network-based methods to identify fetal heart anatomy in classification and segmentation modelling. The reviewed literature uses five categorical technical analysis terms: attention and saliency, coarse to fine, dilated convolution, generative adversarial networks, and spatio-temporal. This review offers a technical overview for those already working in the field and an introduction to those new to the topic.

Authors

  • Netzahualcoyotl Hernandez-Cruz
    Institute of Biomedical Engineering, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, UK. Electronic address: netzahualcoyotl.hernandez-cruz@eng.ox.ac.uk.
  • Olga Patey
    Nuffield Department of Women's & Reproductive Health, University of Oxford, Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
  • Clare Teng
    Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
  • Aris T Papageorghiou
    Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, OX3 9DU, UK.
  • J Alison Noble
    Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, England, UK.