QTcNet, a deep learning model, reduced cross-cohort mean absolute error for QTc measurement from 23.4 to 13.4 ms compared to conventional algorithms across independent validation datasets.
Does QTcNet improve the accuracy of automated QTc measurements compared to conventional algorithms?
A deep learning model, QTcNet, significantly improves the accuracy of automated QTc interval measurements across diverse ECG datasets compared to conventional algorithms.
Absolute Event Rate: 13.4% vs 23.4%
AIMS: Automated QTc measurements from commercial ECG systems often diverge from expert readings. We developed QTcNet, a deep learning model trained and validated on multiple large ECG datasets to improve automated QTc measurement accuracy. METHODS AND RESULTS: QTcNet employs a regression-based convolutional neural network architecture. It was trained on 120 300 algorithm-labelled ECGs (60 150 from an internal hospital cohort and 60 150 from the MIMIC-IV dataset) after correction for a vendor-specific +15 ms bias. Performance was evaluated against expert QTc measurements in three independent datasets: PTB Diagnostic ECG Database (n = 100 ECGs in validation set), QTcMS (n = 210), and ECGRDVQ (n = 5219). The effect of fine-tuning on cardiologist-annotated ECGs was tested in the PTB database (n = 449 in fine-tuning set). Model explainability analyses were performed with Integrated Gradient maps. QTcNet reduced cross-cohort mean absolute error (MAE) from 23.4 to 13.4 ms and root mean square error (RMSE) from 40.1 to 22.1 ms, almost halving large (>50 ms) outliers. Fine-tuning only reduced errors in the PTB dataset but did not improve cross-cohort performance. Integrated Gradient maps confirmed that the model concentrated on QRS onset and T wave offset, supporting physiological plausibility. CONCLUSION: QTcNet, trained on large-scale algorithmically labelled data, consistently outperformed conventional algorithms across three independent, external validation datasets. Fine-tuning of QTcNet may adapt the model to the characteristics of specific cohorts but reduces external validity in other cohorts. We openly release the full model and code, along with a ready-to-use online implementation at https://qtcnet.uni-muenster.de, facilitating further research and community-driven improvement.
Plagwitz et al. (Sat,) conducted a other in ECG QTc interval estimation (n=126,278). QTcNet vs. Conventional algorithms was evaluated on Cross-cohort mean absolute error (MAE). QTcNet, a deep learning model, reduced cross-cohort mean absolute error for QTc measurement from 23.4 to 13.4 ms compared to conventional algorithms across independent validation datasets.