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Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a
Naomi Hirota1, Shinya Suzuki2, Jun Motogi3
1Department of Cardiovascular Medicine, The Cardiovascular Institute, 3-2-19 Nishiazabu, Minato-Ku, Tokyo, 106-0031, Japan. n-hirota@cvi.or.jp.
Insights
Convolutional neural network (CNN)-enhanced electrocardiography (ECG) shows promise for detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM). While initial precision is low, targeting specific patients and using subtype models significantly improves diagnostic accuracy.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) are significant cardiac conditions.
- The real-world diagnostic efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) for HCM and dHCM is not well-established.
- Accurate and early detection of HCM and dHCM is crucial for patient management and outcomes.
Purpose of the Study:
- To evaluate the real-world diagnostic performance of CNN-enhanced ECG for detecting HCM and dHCM.
- To compare the efficacy of a basic diagnosis model versus a comprehensive diagnosis model (including subtypes).
- To assess the impact of different ECG lead configurations on diagnostic accuracy.
Main Methods:
- Retrospective analysis of 19,170 patient ECGs from the Shinken Database (2010-2017), including 140 with HCM or dHCM.
- Development and evaluation of two CNN-enhanced ECG models: a 'basic diagnosis' model and a 'comprehensive diagnosis' model.
- Assessment of sensitivity, positive predictive rate (PPR), and F1 score across different diagnostic probabilities and lead configurations.
Main Results:
- The 'basic diagnosis' model achieved 76% sensitivity, 2.9% PPR, and 0.056 F1 score using all-lead ECG.
- Performance improved significantly in patients with diagnostic probability ≥0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, 0.158 F1 score.
- The 'comprehensive diagnosis' model further enhanced metrics to 100% sensitivity, 13.0% PPR, and 0.230 F1 score. Performance was consistent with fewer leads, especially those viewing lateral walls.
Conclusions:
- CNN-enhanced ECG shows potential for HCM and dHCM detection, with improved accuracy when focusing on high-probability cases and incorporating disease subtypes.
- While initial real-world precision is low, targeted application and comprehensive models enhance diagnostic value.
- ECG configurations including lateral leads are effective, suggesting potential for reduced lead usage without compromising performance.
Abstract:
The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) remains uncertain in real-world applications. This retrospective study analyzed data from 19,170 patients (including 140 HCM or dHCM) in the Shinken Database (2010-2017). We evaluated the sensitivity, positive predictive rate (PPR), and F1 score of CNN-enhanced ECG in a ''basic diagnosis'' model (total disease label) and a ''comprehensive diagnosis'' model (including disease subtypes). Using all-lead ECG in the "basic diagnosis" model, we observed a sensitivity of 76%, PPR of 2.9%, and F1 score of 0.056. These metrics improved in cases with a diagnostic probability of ≥ 0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, and 0.158 F1 score. The ''comprehensive diagnosis'' model further enhanced these figures to 100%, 13.0%, and 0.230, respectively. Performance was broadly consistent across CNN models using different lead configurations, particularly when including leads viewing the lateral walls. While the precision of CNN models in detecting HCM or dHCM in real-world settings is initially low, it improves by targeting specific patient groups and integrating disease subtype models. The use of ECGs with fewer leads, especially those involving the lateral walls, appears comparably effective.
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