ArticleFrontiers in artificial intelligence2026
AI-assisted longitudinal cardiac phenotyping identifies domain-specific remodeling patterns in Fabry cardiomyopathy.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Artificial intelligence in cataract management: current status and future perspectives.International ophthalmology · 2026Review
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9 authors.
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Abstract
Background: Cardiac involvement in Fabry disease spans structural hypertrophy, mechanical dysfunction, and electrical conduction abnormalities that rarely progress in parallel. Although such discordant longitudinal behavior is commonly encountered in clinical practice, it has not been systematically organized within a coherent longitudinal framework. Methods: We conducted a retrospective multicenter longitudinal study of 38 patients managed across three geographically distinct affiliated hospitals within the MacKay Memorial Hospital system, located in Taipei, Tamsui, and Hsinchu. Three major domain-specific remodeling patterns, including structural, mechanical, and electrical domains, were assessed and indexed by left ventricular mass index (LVMi), left ventricular global longitudinal strain (LVGLS), and QRS duration using standard 12-lead electrocardiography (ECG), respectively. All echocardiographic domain measures were conducted using an AI-assisted deep learning-based platform (Us2.ai), which enables standardized longitudinal processing of multi-parametric measurements. Domain-specific longitudinal within-patient changes were classified by the sign of the numerical change (positive vs. negative), with adverse or non-adverse designation defined according to the domain-specific clinical meaning of each parameter, and compared between patients with and without enzyme replacement therapy (ERT). Results: During a median of 5.3 years (interquartile range, 2.8-7.1 years) follow-up, electrical remodeling showed an adverse trend, with QRS duration increasing from 128.21 ± 31.18 ms to 139.63 ± 37.95 ms (Δ + 11.42 ms, 95% CI 5.93-16.92; Conclusion: AI-assisted longitudinal monitoring enables consistent characterization of non-parallel, domain-specific change patterns. Electrical-mechanical phenotypes were heterogeneous, with a significant difference in phenotype distribution between untreated and ERT-treated patients.
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