Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study
| Autor: | LaiTe Chen, GuoSheng Fu, ChenYang Jiang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Annals of Medicine, Vol 55, Iss 1 (2023) |
| Druh dokumentu: | article |
| ISSN: | 07853890 1365-2060 0785-3890 |
| DOI: | 10.1080/07853890.2023.2235564 |
| Popis: | AbstractObjective: Given the psychosocial and ethical burden, patients with hypertrophic cardiomyopathy (HCMs) could benefit from the establishment of genetic probability prior to the test. This study aimed to develop a simple tool to provide genotype prediction for HCMs.Methods: A convolutional neural network (CNN) was built with the 12-lead electrocardiogram (ECG) of 124 HCMs who underwent genetic testing (GT), externally tested by predicting the genotype on another HCMs cohort (n = 54), and compared with the conventional methods (the Mayo and Toronto score). Using a third cohort of HCMs (n = 76), the role of the network in risk stratification was explored by calculating the sudden cardiac death (SCD) risk scorers (HCM risk-SCD) across the predicted genotypes. Score-CAM was employed to provide a visual explanation of the network.Results: Overall, 80 of 178 HCMs (45%) were genotype-positive. Using the 12-lead ECG as input, the network showed an area under the curve (AUC) of 0.89 (95% CI, 0.83–0.96) on the test set, outperforming the Mayo score (0.69 [95% CI, 0.65–0.78], p |
| Databáze: | Directory of Open Access Journals |
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