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Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease.
Liu, Xiangyu; Zhang, Yingying; Zhu, Haogang; Jia, Bosen; Wang, Jingyi; He, Yihua; Zhang, Hongjia.
Affiliation
  • Liu X; School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Zhang Y; Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China.
  • Zhu H; School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Jia B; Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China.
  • Wang J; Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China.
  • He Y; State Key Laboratory of Software Development Environment, Beihang University, Beijing, China.
  • Zhang H; School of Computer Science and Engineering, Beihang University, Beijing, China.
Front Cardiovasc Med ; 11: 1345761, 2024.
Article in En | MEDLINE | ID: mdl-38720920
ABSTRACT
Artificial intelligence (AI) has made significant progress in the medical field in the last decade. The AI-powered analysis methods of medical images and clinical records can now match the abilities of clinical physicians. Due to the challenges posed by the unique group of fetuses and the dynamic organ of the heart, research into the application of AI in the prenatal diagnosis of congenital heart disease (CHD) is particularly active. In this review, we discuss the clinical questions and research methods involved in using AI to address prenatal diagnosis of CHD, including imaging, genetic diagnosis, and risk prediction. Representative examples are provided for each method discussed. Finally, we discuss the current limitations of AI in prenatal diagnosis of CHD, namely Volatility, Insufficiency and Independence (VII), and propose possible solutions.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Cardiovasc Med Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Cardiovasc Med Year: 2024 Document type: Article