ECG data analysis to determine ST-segment elevation myocardial infarction and infarction territory type: an integrative approach of artificial intelligence and clinical guidelines
These results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings.
Authors
- Jungrae Cho (ORCID: https://orcid.org/0000-0003-3600-468X)
- Se Yong Jang (ORCID: https://orcid.org/0000-0002-4981-879X)
- Sungmoon Jeong (ORCID: https://orcid.org/0000-0002-4579-3150)
- Jong-Kwang Kim (ORCID: https://orcid.org/0000-0001-9290-0820)
- Byungeun Shon (ORCID: https://orcid.org/0000-0001-5161-2398)
- Sang‐Wook Kim (ORCID: https://orcid.org/0000-0002-6345-9084)
- Jungju Seo
Institutions
- Kyungpook National University Hospital (KR)
- Kyungpook National University (KR)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2024-10-07
- DOI
- https://doi.org/10.3389/fphys.2024.1462847
- Citations
- 2
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- Korea Health Industry Development Institute
- Iran Telecommunication Research Center
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion