Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification
Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected f...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2024
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| _version_ | 1811140214388686848 |
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| author | Lu, L Zhu, T Ribeiro, AH Clifton, L Zhao, E Zhou, J Ribeiro, ALP Zhang, Y-T Clifton, DA |
| author_facet | Lu, L Zhu, T Ribeiro, AH Clifton, L Zhao, E Zhou, J Ribeiro, ALP Zhang, Y-T Clifton, DA |
| author_sort | Lu, L |
| collection | OXFORD |
| description | Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected from 1,558,772 patients with 7 years of follow-up, we developed a deep learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hyper- tension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (0.963-0.965), and 0.839 (0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis, and the advancement in mortality risk stratification; In addition, the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available. |
| first_indexed | 2024-03-07T08:25:45Z |
| format | Journal article |
| id | oxford-uuid:22984004-6b1d-41a7-b464-f21a69a03f0b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:18:25Z |
| publishDate | 2024 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:22984004-6b1d-41a7-b464-f21a69a03f0b2024-07-25T16:41:09ZDecoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratificationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:22984004-6b1d-41a7-b464-f21a69a03f0bEnglishSymplectic ElementsOxford University Press2024Lu, LZhu, TRibeiro, AHClifton, LZhao, EZhou, JRibeiro, ALPZhang, Y-TClifton, DAElectrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected from 1,558,772 patients with 7 years of follow-up, we developed a deep learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hyper- tension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (0.963-0.965), and 0.839 (0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis, and the advancement in mortality risk stratification; In addition, the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available. |
| spellingShingle | Lu, L Zhu, T Ribeiro, AH Clifton, L Zhao, E Zhou, J Ribeiro, ALP Zhang, Y-T Clifton, DA Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification |
| title | Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification |
| title_full | Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification |
| title_fullStr | Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification |
| title_full_unstemmed | Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification |
| title_short | Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification |
| title_sort | decoding 2 3 million ecgs interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification |
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