Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses.
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| Title: | Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses. |
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| Authors: | Yang ZY; Division of Family Medicine, Department of Family and Community Medicine, Tri-Service General Hospital, and School of Medicine, National Defense Medical University, Taipei, Taiwan, Republic of China.; Graduate Institute of Medical Sciences, National Defense Medical University, Taipei, Taiwan, Republic of China., Hsing SC; Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, and School of Medicine, National Defense Medical University, Taipei, Taiwan, Republic of China., Tsai DJ; Medical Technology Education Center, School of Medicine, National Defense Medical University, Taipei, Taiwan, Republic of China., Lin C; Medical Technology Education Center, School of Medicine, National Defense Medical University, Taipei, Taiwan, Republic of China.; Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, Republic of China.; School of Public Health, National Defense Medical University, Taipei, Taiwan, Republic of China.; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan, Republic of China., Lin CS; Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, and School of Medicine, National Defense Medical University, Taipei, Taiwan, Republic of China., Wang CH; Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, Republic of China.; Graduate Institute of Medical Sciences, National Defense Medical University, Taipei, Taiwan, Republic of China., Fang WH; Division of Family Medicine, Department of Family and Community Medicine, Tri-Service General Hospital, and School of Medicine, National Defense Medical University, Taipei, Taiwan, Republic of China. rumaf.fang@gmail.com.; Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, Republic of China. rumaf.fang@gmail.com.; Department of Education and Research, Taipei City Hospital, Taipei, Taiwan, Republic of China. rumaf.fang@gmail.com. |
| Source: | BioData mining [BioData Min] 2026 Mar 04; Vol. 19 (1). Date of Electronic Publication: 2026 Mar 04. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101319161 Publication Model: Electronic Cited Medium: Print ISSN: 1756-0381 (Print) Linking ISSN: 17560381 NLM ISO Abbreviation: BioData Min Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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