Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND: 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. METHODS: The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; left ventricular hypertrophy controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; left ventricular hypertrophy controls, 222). RESULTS: The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91–0.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87–0.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90–0.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91–0.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91–0.95] versus AUC, 0.88 [95% CI, 0.85–0.90]; P <0.001). 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.
Authors: Adam Ioannou, Michel G. Khouri, Takeshi Kitai, Sreekanth Vemulapalli, Chung-Lieh Hung, Sze Chi Lim, Matthew Frost, Weile Wayne Tee, Josephine Mansell, Awais Sheikh, Lucia Venneri, Yousuf Razvi, Aldostefano Porcari, Ana Martinez-Naharro, Muhammad U. Rauf, Helen Lachmann, Philip N. Hawkins, Ashutosh Wechelakar, William Moody, Francesco Bandera, Justin A. Ezekowitz, Carolyn S.P. Lam, Scott D. Solomon, Julian D. Gillmore, Marianna Fontana
Institutions: The Royal Free Hospital, University College London, Duke University Health System, National Cerebral and Cardiovascular Center, Mackay Medical University, Queen Elizabeth Hospital Birmingham, University of Birmingham, MultiMedica, University of Alberta, National University of Singapore, National University Heart Centre Singapore, National Heart Centre Singapore, Brigham and Women's Hospital, Harvard University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7128704425