ArticleEuropean heart journal2025
Artificial intelligence-enabled electrocardiography and echocardiography to track preclinical progression of transthyretin amyloid cardiomyopathy.
Article in European heart journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study.PLOS digital health · 2026Article
- Artificial intelligence and the evolution of the electrocardiogram: from cardiovascular diagnostic tool to digital biomarker.European heart journal. Digital health · 2026Review
- Artificial intelligence in cardiovascular care: a systematic review and meta-analysis of randomised controlled trials.EClinicalMedicine · 2026Article
- Digital phenotyping of aortic stenosis-related remodeling reveals complementary structural, electrical, and hemodynamic signatures.medRxiv : the preprint server for health sciences · 2026Article
- AI-assisted diagnosis of cardiac amyloidosis using electrocardiograms and echocardiography: a multicenter retrospective study in China.BMC medicine · 2026Article
- Prioritizing Discovery and Advancements in Arrhythmia Therapies: NIH/NHLBI Workshop.JACC. Clinical electrophysiology · 2026Review
- Artificial Intelligence in Cardiac Amyloidosis: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
- Multi-view deep learning for echocardiogram interpretation.Nature cardiovascular research · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
- Precision Medicine for Electrocardiogram Interpretation: Clinical Relevance, Challenges, and Advances.Reviews in cardiovascular medicine · 2025Review
- Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.Circulation · 2025Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- The emerging role of AI in transforming cardiovascular care.Future cardiology · 2025Article
- Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.Journal of the American College of Cardiology · 2025Article
- Multi-scale data reveal a CD24(+) MUCL1(+) tumor subgroup associated with unfavorable prognosis in ER+ breast cancer.Frontiers in immunology · 2025Article
- An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.medRxiv : the preprint server for health sciences · 2024Article
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Abstract
BACKGROUND AND
aimsThe diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale preclinical testing. Artificial intelligence (AI)-enabled transthoracic echocardiography (TTE) and electrocardiography (ECG) may provide a scalable strategy for preclinical monitoring.
methodsThis was a retrospective analysis of individuals referred for nuclear cardiac amyloid testing at the Yale-New Haven Health System (YNHHS, internal cohort) and Houston Methodist Hospitals (HMH, external cohort). Deep learning models trained to discriminate ATTR-CM from age/sex-matched controls on TTE videos (AI-Echo) and ECG images (AI-ECG) were deployed to generate study-level ATTR-CM probabilities (0%-100%). Longitudinal trends in AI-derived probabilities were examined using age/sex-adjusted linear mixed models, and their discrimination of future disease was evaluated across preclinical stages.
resultsAmong 984 participants at YNHHS (median age 74 years, 44.3% female) and 806 at HMH (median age 69 years, 34.5% female), 112 (11.4%) and 174 (21.6%) tested positive for ATTR-CM, respectively. Across cohorts and modalities, AI-derived ATTR-CM probabilities from 7352 TTEs and 32 205 ECGs diverged as early as 3 years before diagnosis in cases vs controls (ptime(x)group interaction ≤ .004). Among those with both AI-Echo and AI-ECG probabilities available 1 to 3 years before nuclear testing [n = 433 (YNHHS) sand 174 (HMH)], a double-negative screen at a 0.05 threshold [164 (37.9%) and 66 (37.9%), vs all else] had 90.9% and 85.7% sensitivity (specificity of 40.3% and 41.2%), whereas a double-positive screen [78 (18.0%) and 26 (14.9%), vs all else] had 85.5% and 88.9% specificity (sensitivity of 60.6% and 42.9%).
conclusionsArtificial intelligence-enabled echocardiography and electrocardiography may enable scalable risk stratification of ATTR-CM during its preclinical course.
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