Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to:
(1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA;
(2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography.
CONCLUSIONS:
Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.
Ioannou, A., Khouri, M. G., Kitai, T., Vemulapalli, S., Hung, C.-L., Lim, S. C., Frost, M., Tee, W. W., Mansell, J., Sheikh, A., Venneri, L., Razvi, Y., Porcari, A., Martinez-Naharro, A., Rauf, M. U., Lachmann, H., Hawkins, P. N., Wechelakar, A., Moody, W., & Bandera, F. (2026). Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence. Circulation: Cardiovascular Imaging. https://doi.org/10.1161/circimaging.125.018991