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Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
Wei-Yin Ko1, Konstantinos C Siontis1, Zachi I Attia1
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
Insights
Artificial intelligence can detect hypertrophic cardiomyopathy (HCM) using electrocardiography (ECG) with high accuracy, especially in younger individuals. This AI model shows promise for future HCM screening applications.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Hypertrophic cardiomyopathy (HCM) is a significant, though uncommon, cause of sudden cardiac death.
- Early and accurate detection of HCM is crucial for patient management and risk stratification.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) approach for detecting hypertrophic cardiomyopathy (HCM) using 12-lead electrocardiography (ECG).
Main Methods:
- A convolutional neural network (CNN) was trained on ECG data from 2,448 HCM patients and 51,153 controls.
- The CNN model was validated and tested on separate datasets, evaluating its diagnostic performance using area under the curve (AUC), sensitivity, and specificity.
Main Results:
- The AI model achieved a high AUC of 0.96 in the testing dataset, with 87% sensitivity and 90% specificity.
- The model demonstrated strong performance across various subgroups, including patients with ECG-defined left ventricular hypertrophy and normal ECGs.
- Performance was particularly notable in younger patients (95% sensitivity, 92% specificity).
Conclusions:
- AI-driven ECG analysis can effectively detect HCM with high diagnostic performance.
- The developed AI model shows potential for HCM screening, particularly in younger populations, but requires further validation.
Background:
Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.
Objectives:
This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG).
Methods:
A convolutional neural network (CNN) was trained and validated using digital 12-lead ECG from 2,448 patients with a verified HCM diagnosis and 51,153 non-HCM age- and sex-matched control subjects. The ability of the CNN to detect HCM was then tested on a different dataset of 612 HCM and 12,788 control subjects.
Results:
In the combined datasets, mean age was 54.8 ± 15.9 years for the HCM group and 57.5 ± 15.5 years for the control group. After training and validation, the area under the curve (AUC) of the CNN in the validation dataset was 0.95 (95% confidence interval [CI]: 0.94 to 0.97) at the optimal probability threshold of 11% for having HCM. When applying this probability threshold to the testing dataset, the CNN's AUC was 0.96 (95% CI: 0.95 to 0.96) with sensitivity 87% and specificity 90%. In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with left ventricular hypertrophy by ECG criteria and 0.95 (95% CI: 0.90 to 1.00) among patients with a normal ECG. The model performed particularly well in younger patients (sensitivity 95%, specificity 92%). In patients with HCM with and without sarcomeric mutations, the model-derived median probabilities for having HCM were 97% and 96%, respectively.
Conclusions:
ECG-based detection of HCM by an artificial intelligence algorithm can be achieved with high diagnostic performance, particularly in younger patients. This model requires further refinement and external validation, but it may hold promise for HCM screening.
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