AI Article Synopsis
- Acute Coronary Syndrome (ACS), particularly STEMI, is a major global health issue, and this study introduces a deep learning AI method for precise diagnosis of STEMI using 12-lead ECG data.
- The AI model was validated with a database of 888 MI patients, achieving high accuracy in detecting ST-segment elevation and differentiating between STEMI and NSTEMI with impressive AUROC and AUPRC scores.
- The findings indicate that the integration of AI into clinical ECG diagnostics can significantly enhance the timely treatment and outcomes for STEMI patients, underscoring the potential of AI in healthcare.
Article Abstract
Introduction: Acute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm.
Methods: Utilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI).
Results: In the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease.
Discussion: 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491539 | PMC |
| http://dx.doi.org/10.3389/fphys.2024.1462847 | DOI Listing |
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