XplainScar: Explainable Artificial Intelligence to Identify and Localize Left Ventricular Scar in Hypertrophic Cardiomyopathy from 12-lead Electrocardiogram
Bibliographic record
Abstract
Abstract Left ventricular (LV) scar is a risk factor for sudden cardiac death and heart failure in hypertrophic cardiomyopathy (HCM). LV scar is frequent in HCM and evolves over time. Hence there is a need for LV scar detection and longitudinal monitoring. The current gold standard for LV scar detection is late gadolinium enhancement (LGE) on magnetic resonance imaging (MRI), which is limited by high cost and susceptibility to artifacts from implanted defibrillators. We introduce XplainScar , the first explainable machine learning method for LV scar detection and localization in HCM, using 12-lead electrocardiogram (ECG) data, which is not influenced by implanted devices. We use 500 patients from the JH-HCM Registry for model development, and 248 patients from the UCSF-HCM-Registry for validation. XplainScar combines unsupervised and self-supervised ECG representation learning, resulting in high precision (90%), sensitivity (95%), specificity (80%) and F1-score (90%) for scar detection in the basal, mid, and apical LV myocardium, with a processing time of <1 minute per 10 patients. Basal LV scar prediction by XplainScar is dominated by QRS features, and mid/apical LV scar by T wave features. XplainScar generalizes well to the held-out test UCSF data, with 88% precision, 90% sensitivity, 78% specificity, and F1-score of 89%. In summary, XplainScar demonstrates good performance for LV scar detection, and provides ECG signatures of basal, mid, and apical LV scar in HCM. XplainScar is publicly available at https://github.com/KasraNezamabadi/XplainScar
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".