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

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Tác giả: Netzahualcoyotl Hernandez-Cruz, J Alison Noble, Aris T Papageorghiou, Olga Patey, Clare Teng

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: United States : Computers in biology and medicine , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 190481

 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.
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