Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study
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 neu...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Annals of Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2023.2235564 |
| _version_ | 1827380472264523776 |
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| author | LaiTe Chen GuoSheng Fu ChenYang Jiang |
| author_facet | LaiTe Chen GuoSheng Fu ChenYang Jiang |
| author_sort | LaiTe Chen |
| collection | DOAJ |
| description | 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 < 0.001) and the Toronto score (0.69 [95% CI, 0.64–0.75], p < 0.001). The network classified the third cohort into two groups (predicted genotype-negative vs. predicted genotype-positive). Compared with the former, patients predicted genotype-positive had a significantly higher HCM risk-SCD (0.04 ± 0.03 vs. 0.03 ± 0.02, p <0.01). Visualization indicated that the prediction was heavily influenced by the limb lead.Conclusions: The network demonstrated a promising ability in genotype prediction and risk assessment in HCM. |
| first_indexed | 2024-03-08T13:35:20Z |
| format | Article |
| id | doaj.art-6060da75db7e44018192f24cf4ee3118 |
| institution | Directory Open Access Journal |
| issn | 0785-3890 1365-2060 |
| language | English |
| last_indexed | 2024-03-08T13:35:20Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj.art-6060da75db7e44018192f24cf4ee31182024-01-16T19:13:22ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602023-12-0155110.1080/07853890.2023.2235564Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot studyLaiTe Chen0GuoSheng Fu1ChenYang Jiang2The Affiliated Eye Hospital of Wenzhou Medical University, Wenzhou, P.R. ChinaSir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, P.R. ChinaSir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, P.R. ChinaAbstractObjective: 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 < 0.001) and the Toronto score (0.69 [95% CI, 0.64–0.75], p < 0.001). The network classified the third cohort into two groups (predicted genotype-negative vs. predicted genotype-positive). Compared with the former, patients predicted genotype-positive had a significantly higher HCM risk-SCD (0.04 ± 0.03 vs. 0.03 ± 0.02, p <0.01). Visualization indicated that the prediction was heavily influenced by the limb lead.Conclusions: The network demonstrated a promising ability in genotype prediction and risk assessment in HCM.https://www.tandfonline.com/doi/10.1080/07853890.2023.2235564Genotypehypertrophic cardiomyopathyelectrocardiographygenetic testingdeep learningconvolutional neural network |
| spellingShingle | LaiTe Chen GuoSheng Fu ChenYang Jiang Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study Annals of Medicine Genotype hypertrophic cardiomyopathy electrocardiography genetic testing deep learning convolutional neural network |
| title | Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study |
| title_full | Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study |
| title_fullStr | Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study |
| title_full_unstemmed | Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study |
| title_short | Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study |
| title_sort | deep learning derived 12 lead electrocardiogram based genotype prediction for hypertrophic cardiomyopathy a pilot study |
| topic | Genotype hypertrophic cardiomyopathy electrocardiography genetic testing deep learning convolutional neural network |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2023.2235564 |
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