Prospects for using artificial intelligence to detect Spina Bifida in fetuses, children, and adults

Background and Objective: Spina Bifida (SB) is a congenital spinal malformation requiring timely diagnosis. Digitalization of healthcare and rapid development of artificial intelligence technologies (AI) facilitate AI implementation in various fields of medicine, including SB diagnosis. The aim of this study is to analyze the potential of AI for detecting signs of Spina Bifida in fetuses, children, and adults, evaluate existing approaches, and identify key areas for further research and development.

Methods: this review includes original and review publications, conference materials describing the application of AI algorithms to any type of data at any stage of the diagnostic process of SB in fetuses, children, and adults. A search of available literature was conducted in the following databases: PubMed, Google Scholar, and the Russian Science Citation Index (eLibrary.ru).

Results: seventeen publications on the use of AI in the diagnosis of SB were selected, which met the search criteria. Using AI algorithms, the following types of data were identified and analyzed: medical imaging data (magnetic resonance imaging, ultrasound, X-ray, video urodynamics), laboratory diagnostics data, and genetic information.

Discussion: AI algorithms have shown high efficiency in detecting SB and its complications at various stages of the diagnostic process. The prospect of using computer vision to detect SB in images of various modalities as well as machine learning algorithms in laboratory diagnostics and genetic research was demonstrated.

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