Evidence map›Paper›PMID 41028011›Full record

ArticleScientific reports2025

Explainable artificial intelligence identifies and localizes left ventricular scar in hypertrophic cardiomyopathy using 12-Lead electrocardiogram.

Kasra Nezamabadi, Sanjay Sivalokanathan, Jiwon Lee, Talha Tanriverdi, Meiling Chen, Daiyin Lu, Jadyn Abraham, Neda Sardaripour, Pengyuan Li, Parvin Mousavi and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Kasra NezamabadiComputational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, DE, USA.
Sanjay SivalokanathanDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California, San Francisco, 555 Mission Bay Blvd South, San Francisco, CA, 94127, USA.
Jiwon LeeDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California, San Francisco, 555 Mission Bay Blvd South, San Francisco, CA, 94127, USA.
Talha TanriverdiDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California, San Francisco, 555 Mission Bay Blvd South, San Francisco, CA, 94127, USA.
Meiling ChenDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California, San Francisco, 555 Mission Bay Blvd South, San Francisco, CA, 94127, USA.
Daiyin LuDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California, San Francisco, 555 Mission Bay Blvd South, San Francisco, CA, 94127, USA.
Jadyn AbrahamDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California, San Francisco, 555 Mission Bay Blvd South, San Francisco, CA, 94127, USA.
Neda SardaripourDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
Pengyuan LiIBM Research - Almaden Lab, San Jose, CA, USA.
Parvin MousaviSchool of Computing, Queen's University, Kingston, ON, Canada.
M Roselle AbrahamDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California, San Francisco, 555 Mission Bay Blvd South, San Francisco, CA, 94127, USA. roselle.abraham@ucsf.edu.

Funding

NSF 1650851
6 · The paper itself

Abstract

Left ventricular (LV) scar is a major risk factor for sudden death and heart failure in hypertrophic cardiomyopathy (HCM). LV scar evolves over time and needs longitudinal assessment. Currently, LV scar detection relies on late gadolinium enhancement MRI, which is limited by high cost and artifacts from implanted cardiac devices. To address this, we developed XplainScar, an explainable machine learning model that identifies LV scar using 12-lead electrocardiogram (ECG) data. XplainScar was trained and validated on retrospective data from 748 HCM patients across two centers (500 from Johns Hopkins hospital for model development, and 248 from UCSF for validation). XplainScar employs a combination of unsupervised and self-supervised representation learning to effectively predict scar presence, and discover ECG features associated with LV scar. XplainScar rapidly analyzes ECG data (< 1 min for 10 patients) and demonstrates strong predictive performance on the held-out test set, achieving an F1-score of 89%, sensitivity of 90%, specificity of 78%, and precision of 88%. By providing an effective, cost-effective, and transparent alternative to MRI, XplainScar has the potential to assist with patient care, and reduce healthcare costs related to LV scar monitoring in HCM. XplainScar is available at https://github.com/KasraNezamabadi/XplainScar .

Indexed as

Artificial IntelligenceCardiomyopathy, HypertrophicCicatrixElectrocardiographyHeart VentriclesAdultAgedFemaleHumansMachine LearningMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesExplainable AIHypertrophic cardiomyopathyLeft ventricular scarMachine learningSelf-supervised learningUnsupervised clustering

Identifiers

PMID41028011
PMCPMC12484639

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.