Abstract 13832: XplainScar: Explainable Artificial Intelligence to Identify and Localize Left Ventricular Scar in Hypertrophic Cardiomyopathy (HCM) Using 12-lead Electrocardiogram
Bibliographic record
Abstract
Background/Rationale: Myocardial scar, identified by late gadolinium enhancement (LGE) on MRI, is associated with sudden death in HCM. Unlike ECG, MRI is expensive and adversely affected by artifacts from implanted devices. However, little is known about ECG features of LV-scar in HCM. Objective: Develop an ECG-based explainable machine learning method to identify and localize LV-scar in HCM. Method: We retrospectively studied 500 HCM patients (JH HCM Registry) for model development, and 248 patients (UCSF HCM Registry) for validation. All patients underwent MRI and ECHO within 1 year of ECG. LV-LGE (scar) was assessed using QMass. After excluding RV-insertion-point-LGE, the LV was divided into basal, mid, and apical regions for scar detection. Resting 12-lead ECGs were segmented, features were extracted and adjusted for LV-mass, age, sex. We utilized unsupervised and self-supervised ECG representation learning, where patients are partitioned into groups of several sub-clusters, each sharing similar ECG patterns, but with high separation between scar and no-scar classes. In each group, a self-supervised neural net and a fully connected neural net successfully predicted LV-scar (see Figure) and revealed ECG features of scar. Results: Our method identifies LV-scar in the JH-dataset with high precision (90%), sensitivity (95%), specificity (80%), F1-score (90%), and generalizes well to UCSF-data (precision:88%, sensitivity:90%, specificity:78%, F1-score:89%). The top ECG features for basal-scar are Q-amplitude, Q-slope, non-terminal QRS duration in aVR, and area under QRS and T wave energy in V1-V2. T-wave inversion in V4-V6, area under QRS in V3, TP slope in V3-V4 predicted apical scar. Features selected for mid scar prediction combine features for basal and apical scar. Conclusion: This is the first ECG-based ML model to identify/localize LV-scar in HCM. Our model demonstrates good performance and reveals ECG features of scar in HCM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.005 | 0.001 |
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".