Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence
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
- Josephine Mansell (ORCID: https://orcid.org/0000-0002-1778-3713)
- Ashutosh Wechelakar
- Lucia Venneri (ORCID: https://orcid.org/0009-0006-2551-7403)
- Michel G. Khouri (ORCID: https://orcid.org/0009-0002-6235-5176)
- Yousuf Razvi (ORCID: https://orcid.org/0000-0003-4766-3293)
- J Ezekowitz
- William E. Moody (ORCID: https://orcid.org/0000-0002-2592-618X)
- Francesco Bandera
- See Lim
- Adam Ioannou
- T Kitai
- H J Lachmann
- M Rauf
- S Vemulapalli
- P Hawkins
- Marianna Fontana
- Aldostefano Porcari
- Carolyn S.P. Lam
- Ana Martinez-Naharro
- Julian D. Gillmore
- Scott D. Solomon
- Awais Sheikh
- Weile Wayne Tee
- Chung-Lieh Hung
- Matthew Frost
Institutions
- Brigham and Women's Hospital (US)
- University of Alberta (CA)
- University of Milan (IT)
- Queen Elizabeth Hospital Birmingham (GB)
- Mackay Medical University (TW)
- Clinical Research Institute (US)
- The Edgbaston Hospital (GB)
- MultiMedica (IT)
- National University Heart Centre Singapore (SG)
- Amyloidosis Foundation (US)
- National Cerebral and Cardiovascular Center (JP)
- Duke University Health System (US)
- University College London (GB)
- University of Birmingham (GB)
- Wipro (Singapore) (SG)
Publication Details
- Journal
- Circulation Cardiovascular Imaging
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1161/circimaging.125.018991
- Primary Topic
- Amyloidosis: Diagnosis, Treatment, Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00