CODE-II: A large-scale dataset for artificial intelligence in ECG analysis
This provides a high-quality dataset for researchers in medical AI and ECG analysis, though it is incremental as it builds on existing data-driven methods.
The authors tackled the problem of limited annotation quality, size, and scope in AI-based ECG analysis by introducing CODE-II, a large-scale dataset of 2,735,269 ECGs with 66 diagnostic classes, and a neural network pre-trained on it achieved superior transfer performance on external benchmarks.
Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligence (AI) based ECG analysis, yet limitations in annotation quality, size, and scope remain major challenges. Here we present CODE-II, a large-scale real-world dataset of 2,735,269 12-lead ECGs from 2,093,807 adult patients collected by the Telehealth Network of Minas Gerais (TNMG), Brazil. Each exam was annotated using standardized diagnostic criteria and reviewed by cardiologists. A defining feature of CODE-II is a set of 66 clinically meaningful diagnostic classes, developed with cardiologist input and routinely used in telehealth practice. We additionally provide an open available subset: CODE-II-open, a public subset of 15,000 patients, and the CODE-II-test, a non-overlapping set of 8,475 exams reviewed by multiple cardiologists for blinded evaluation. A neural network pre-trained on CODE-II achieved superior transfer performance on external benchmarks (PTB-XL and CPSC 2018) and outperformed alternatives trained on larger datasets.