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Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an
Konstantinos C Siontis1, Mikolaj A Wieczorek2, Maren Maanja1,3
1Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Insights
An artificial intelligence (AI) model accurately detected hypertrophic cardiomyopathy (HCM) using electrocardiograms (ECG) in diverse international patient groups. This AI-ECG algorithm shows strong external validity for identifying HCM from ECG data alone.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Deep learning AI models are increasingly used for cardiovascular condition detection.
- Electrocardiograms (ECG) are a common tool for assessing heart health.
- Hypertrophic cardiomyopathy (HCM) is a significant cardiovascular condition requiring accurate detection.
Purpose of the Study:
- To externally validate an AI-ECG algorithm for detecting hypertrophic cardiomyopathy (HCM).
- To assess the algorithm's performance across diverse international patient cohorts.
- To evaluate the algorithm's accuracy in distinguishing HCM from non-HCM using ECG data.
Main Methods:
- A convolutional neural network-based AI-ECG algorithm, previously developed, was applied to 12-lead ECG data.
- The algorithm was tested on external validation cohorts from Switzerland, the UK, and South Korea.
- Performance metrics including AUC, accuracy, sensitivity, and specificity were analyzed.
Main Results:
- The study included 773 patients with HCM and 3867 controls across three international sites.
- The AI-ECG algorithm achieved an overall AUC of 0.922 for HCM detection.
- High diagnostic accuracy (86.9%), sensitivity (82.8%), and specificity (87.7%) were observed.
Conclusions:
- The AI-ECG algorithm demonstrated high accuracy in detecting HCM across diverse international cohorts, confirming its external validity.
- The findings support the potential utility of this AI tool for HCM screening and clinical practice.
- Further prospective evaluation is recommended to establish its clinical impact.
Aims:
Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts.
Methods And Results:
A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm's ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910-0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case-control ratio 1:2), the AUC was 0.921 (95% CI 0.909-0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%.
Conclusion:
The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.
