Key result
A deep learning-based ECG diagnostic model achieved high F1 scores for common patterns, such as sinus rhythm (98.01%), and exhibited better efficiency than manual identification.
Why the study?
To investigate a deep learning-based ECG diagnosis system to enable the intelligent diagnosis of a variety of common ECG anomalies.
Does a deep learning-based ECG diagnosis system improve diagnostic accuracy and efficiency compared to manual identification for conventional 12-lead ECGs?
Observational (n=102,620)
No
Does a deep learning-based ECG diagnosis system improve diagnostic accuracy and efficiency compared to manual identification for conventional 12-lead ECGs?
A deep learning-based ECG diagnostic model demonstrates high accuracy for common arrhythmias and improves efficiency over manual interpretation, highlighting its potential to augment clinical workflows.
OBJECTIVE: To enable the intelligent diagnosis of a variety of common Electrocardiogram (ECG), we investigate the deep learning-based ECG diagnosis system. METHODS: From January 2015 to December 2019, four consecutive years of 100,120 conventional 12-lead ECG data were collected in our hospital. Utilizing this dataset, we constructed a deep learning model designed to intelligently diagnose prevalent ECG anomalies by employing a multi-task learning framework. The system performance was evaluated using various metrics, including sensitivity, specificity, negative predictive value, positive predictive value, and so forth. Additionally, we employed an ECG intelligent diagnostic platform for clinical application to undertake real-time online analysis of 2500 conventional 12-lead ECG samples in June 2020, aiming to validate our model. At this stage, we compared the performance of our model against the traditional manual identification method. RESULTS: The efficacy of the ECG intelligent diagnostic model was notably high for common and straightforward ECG patterns, such as sinus rhythm (F1 = 98.01%), sinus tachycardia (F1 = 96.26%), sinus bradycardia (F1 = 94.88%), and a normal electrocardiogram (F1 = 91.71%), as well as for Premature Ventricular Contractions (F1 = 91.62%). Nevertheless, when diagnosing rarer and more intricate ECG anomalies, the system requires an increased number of samples to refine the deep learning models. During the validation stage, our model exhibited better efficiency in terms of accuracy, labor time and labor cost when compared to the manual identification approach. CONCLUSIONS: Our deep learning-driven intelligent ECG diagnostic model clearly demonstrates significant clinical utility. The integrated artificial intelligence diagnosis system not only has the potential to augment physicians in their diagnostic processes but also offers a viable avenue to reduce associated labor costs.
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Li et al. (2023) conducted an observational in Electrocardiogram (ECG) anomalies (n=102,620). Deep learning-based ECG diagnosis system vs. Traditional manual identification method was evaluated on Diagnostic efficacy (F1 score) for common ECG patterns. A deep learning-based ECG diagnostic model achieved high F1 scores for common patterns, such as sinus rhythm (98.01%), and exhibited better efficiency than manual identification.
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