QTcNet

Regression-based convolutional neural network that directly estimates the heart-rate-corrected QT interval (QTc) from a 12-lead ECG, aiming to improve on automated QTc measurements from commercial ECG systems, which often diverge from expert readings. QTcNet was trained on 120,300 algorithm-labeled ECGs from an internal hospital cohort and the public MIMIC-IV database (after correcting for a vendor-specific measurement bias), and evaluated against expert QTc measurements in three independent external cohorts (PTB Diagnostic ECG Database, QTcMS, and ECGRDVQ). It roughly halved the mean absolute error compared with standard ECG analysis software (from 23.4ms to 13.4ms across external validation cohorts), with explainability analysis confirming the model focuses on physiologically plausible QRS-onset and T-offset regions.