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Published on: May 23, 2021
A deep learning approach identifies new ECG features in congenital long QT syndrome
Simona Aufiero1,2, Hidde Bleijendaal3,4, Tomas Robyns5
1Department of Experimental Cardiology, Amsterdam UMC, Amsterdam, The Netherlands. s.aufiero@amsterdamumc.nl.
Insights
Deep learning models show promise in diagnosing congenital long QT syndrome (LQTS) using ECGs, potentially aiding cardiologists and identifying new diagnostic features. These AI tools offer improved accuracy and understanding of this rare heart condition.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Congenital long QT syndrome (LQTS) is a rare, potentially life-threatening heart condition.
- Underdiagnosis of LQTS occurs due to subtle ECG features and limited physician exposure.
- Accurate and timely diagnosis is crucial for managing LQTS and preventing adverse events.
Purpose of the Study:
- To develop and validate deep learning (DL) models for identifying LQTS from electrocardiogram (ECG) data.
- To compare the performance of DL models against conventional QTc measurements and expert cardiologists.
- To utilize explainable AI to uncover novel ECG features associated with LQTS.
Main Methods:
- 1D convolutional neural network models were trained on large ECG datasets from Amsterdam UMC and validated at University Hospital Leuven.
- The datasets included patients with LQTS (types 1, 2, and 3) and control subjects.
- Model performance was evaluated using sensitivity, specificity, and AUC, and compared to QTc measurements and expert diagnosis.
Main Results:
- The best DL models achieved high performance metrics (e.g., 84-90% sensitivity, 92-96% specificity) for LQTS detection.
- DL models outperformed conventional QTc measurements and demonstrated comparable sensitivity but superior specificity to an expert cardiologist.
- Explainable AI identified the QRS complex onset as a key feature for LQTS classification, a previously unrecognized indicator.
Conclusions:
- Deep learning models can effectively aid cardiologists in diagnosing congenital long QT syndrome.
- Explainable AI can reveal new ECG-based biomarkers for LQTS, enhancing diagnostic capabilities.
- These AI-driven approaches hold significant potential for improving the management of LQTS.
Background:
Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying mutations. Most general cardiologists do not routinely see patients with congenital LQTS and may not always recognize the accompanying ECG features. In addition, a proportion of disease carriers do not display obvious abnormalities on their ECG. Combined, this can cause underdiagnosing of this potentially life-threatening disease.
Methods:
This study presents 1D convolutional neural network models trained to identify genotype positive LQTS patients from electrocardiogram as input. The deep learning (DL) models were trained with a large 10-s 12-lead ECGs dataset provided by Amsterdam UMC and externally validated with a dataset provided by University Hospital Leuven. The Amsterdam dataset included ECGs from 10000 controls, 172 LQTS1, 214 LQTS2, and 72 LQTS3 patients. The Leuven dataset included ECGs from 2200 controls, 32 LQTS1, and 80 LQTS2 patients. The performance of the DL models was compared with conventional QTc measurement and with that of an international expert in congenital LQTS (A.A.M.W). Lastly, an explainable artificial intelligence (AI) technique was used to better understand the prediction models.
Results:
Overall, the best performing DL models, across 5-fold cross-validation, achieved on average a sensitivity of 84 ± 2%, 90 ± 2% and 87 ± 6%, specificity of 96 ± 2%, 95 ± 1%, and 92 ± 4%, and AUC of 0.90 ± 0.01, 0.92 ± 0.02, and 0.89 ± 0.03, for LQTS 1, 2, and 3 respectively. The DL models were also shown to perform better than conventional QTc measurements in detecting LQTS patients. Furthermore, the performances held up when the DL models were validated on a novel external cohort and outperformed the expert cardiologist in terms of specificity, while in terms of sensitivity, the DL models and the expert cardiologist in LQTS performed the same. Finally, the explainable AI technique identified the onset of the QRS complex as the most informative region to classify LQTS from non-LQTS patients, a feature previously not associated with this disease.
Conclusions:
This study suggests that DL models can potentially be used to aid cardiologists in diagnosing LQTS. Furthermore, explainable DL models can be used to possibly identify new features for LQTS on the ECG, thus increasing our understanding of this syndrome.
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