July 17, 2025European Heart Journal16 citationsOpen Access
View Full PaperArtificial intelligence-enabled electrocardiography and echocardiography to track preclinical progression of transthyretin amyloid cardiomyopathy
Structured PICO
Do AI-enabled echocardiography and electrocardiography track preclinical progression and predict future diagnosis of ATTR-CM in individuals referred for nuclear cardiac amyloid testing?
P
Population
1,790 individuals referred for nuclear cardiac amyloid testing (984 at Yale-New Haven Health System, 806 at Houston Methodist Hospitals)
I
Intervention
Deep learning models applied to transthoracic echocardiography videos (AI-Echo) and electrocardiography images (AI-ECG) to generate study-level ATTR-CM probabilities
C
Comparator
Age/sex-matched controls (for model training) and standard nuclear testing (as reference standard)
O
Outcome
Longitudinal trends in AI-derived probabilities and their discrimination of future disease across preclinical stagessurrogate
AI-enabled echocardiography and electrocardiography can detect preclinical progression of transthyretin amyloid cardiomyopathy up to 3 years before clinical diagnosis, offering a scalable strategy for early risk stratification.
Abstract
BACKGROUND AND AIMS: The 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. METHODS: This 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. RESULTS: Among 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%). CONCLUSIONS: Artificial intelligence-enabled echocardiography and electrocardiography may enable scalable risk stratification of ATTR-CM during its preclinical course.