TY - JOUR
T1 - Artificial intelligence-enabled electrocardiography and echocardiography to track preclinical progression of transthyretin amyloid cardiomyopathy
AU - Oikonomou, Evangelos K.
AU - Sangha, Veer
AU - Shankar, Sumukh Vasisht
AU - Coppi, Andreas
AU - Krumholz, Harlan M.
AU - Nasir, Khurram
AU - Miller, Edward J.
AU - Gallegos Kattan, Cesia
AU - Al-Mallah, Mouaz H.
AU - Al-Kindi, Sadeer
AU - Khera, Rohan
N1 - Publisher Copyright:
© 2025 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact [email protected] for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site - for further information please contact [email protected].
PY - 2025/10/1
Y1 - 2025/10/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cardiac amyloidosis
KW - Echocardiography
KW - Electrocardiography
KW - Screening
KW - Transthyretin
UR - https://www.scopus.com/pages/publications/105017473284
UR - https://www.scopus.com/inward/citedby.url?scp=105017473284&partnerID=8YFLogxK
U2 - 10.1093/eurheartj/ehaf450
DO - 10.1093/eurheartj/ehaf450
M3 - Article
C2 - 40679604
AN - SCOPUS:105017473284
SN - 0195-668X
VL - 46
SP - 3651
EP - 3662
JO - European heart journal
JF - European heart journal
IS - 37
ER -