Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study
Rattachement africain : kr, Nigéria, us, jp. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Background: Artificial intelligence-augmented electrocardiogram (AI-ECG) models for detecting left ventricular systolic dysfunction (LVSD) often exhibit degraded performance in patients with comorbidities. Objective: This study aimed to introduce and validate a recalibration method using longitudinal patient data to enhance prediction accuracy and simulate its clinical utility for ongoing monitoring. Methods: We conducted a multicenter, retrospective cohort study using data from 2 hospitals in Korea. A dataset of paired transthoracic echocardiograms (TTEs) and electrocardiograms (ECGs) matched within a 2-week interval was constructed, separating pairs into baseline (first for each patient) and follow-up assessments. In addition to conventional supervised learning, we developed a patient-wise recalibration strategy that incorporated historical left ventricular ejection fraction measurements and prior AI-ECG outputs to adjust for future predictions, thus empirically mitigating confounding effects. Pretraining was also implemented to enhance the model's performance. Results: The recalibrated 12-lead DeepECG LVSD model achieved an area under the receiver operating curve of 0.956 (95% CI 0.946-0.965) for internal validation and 0.940 (95% CI 0.936-0.945) for external validation of follow-up TTE-ECG pairs. The uncalibrated 12-lead DeepECG LVSD model also showed modest performance, with an area under the receiver operating curve of 0.953 (95% CI 0.941-0.965) in the internal validation and 0.947 (95% CI 0.943-0.951) in the external validation when tested on baseline TTE-ECG pairs. Recalibration yielded statistically significant improvements in the 12-lead DeepECG LVSD models (P<.001), with enhanced and more balanced performance across all clinical subgroups. Conclusions: Patient-wise recalibration improved accuracy and consistency across various comorbidities by mitigating performance degradation and bias. This broadens the application of AI-ECG for LVSD detection from low-risk screening to high-risk longitudinal monitoring.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study
- Date Crossref
- 24/04/2026
- Éditeur
- JMIR Publications Inc.
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Yonsei University Department of Internal Medicine pays non établi dans la noticeUniversité ou école supérieure
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Wonju Severance Christian Hospital pays non établi dans la noticeÉtablissement de santé
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Sacred Heart Hospital Nigéria (code pays fourni par la source)Établissement de santé
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Sacred Heart Hospital pays non établi dans la noticeÉtablissement de santé
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Hallym University Sacred Heart Hospital pays non établi dans la noticeÉtablissement de santé
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Korea University Department of Internal Medicine pays non établi dans la noticeUniversité ou école supérieure
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Korea University Anam Hospital pays non établi dans la noticeÉtablissement de santé
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VUNO Inc pays non établi dans la noticeEntreprise
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Hallym University College of Medicine Department of Internal Medicine pays non établi dans la noticeUniversité ou école supérieure
Department of Internal Medicine — Yonsei University, Wonju Severance Christian Hospital et Sacred Heart Hospital (Nigéria), avec 6 autres affiliations. Pays d’affiliation : Nigéria.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.