Artificial Intelligence-Based Frailty Assessment Outperforms Clinical Assessment in Predicting Outcomes After Transcatheter Aortic Valve Implantation - 03/03/26
Doi : 10.1016/j.hlc.2025.12.015
Annora Ai-Wei Kumar, MD a, Gavin Huangfu, MD a, b, c, Jake Kendrick, PhD d, e, Jeremy Sze Luong Ong, MBBS f, Gerald Yong, MBBS c, Sharad Shetty, MBBS, MD c, Nick Si Rui Lan, MBBS, MClinUS, MClinRes(Dist), MSc a, b, c, Abdul Rahman Ihdayhid, MBBS, PhD b, c, g, Girish Dwivedi, MD, PhD a, b, c, h, ⁎ 
a Medical School, The University of Western Australia, Crawley, WA, Australia
b Harry Perkins Institute of Medical Research, Murdoch, WA, Australia
c Department of Cardiology, Fiona Stanley Hospital, Murdoch, WA, Australia
d School of Physics, Mathematics, and Computing, The University of Western Australia, Crawley, WA, Australia
e Centre for Advanced Technologies in Cancer Research, Perth, WA, Australia
f Department of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, WA, Australia
g Medical School, Curtin University, Perth, WA, Australia
h CardioInnovate Translational Lab - WA, Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, NSW, Australia
∗ Corresponding author at: Harry Perkins Institute of Medical Research and Fiona Stanley Hospital, The University of Western Australia, Perth, Australia Harry Perkins Institute of Medical Research and Fiona Stanley Hospital The University of Western Australia Perth Australia
Sous presse. Épreuves corrigées par l'auteur.
Disponible en ligne depuis le Tuesday 03 March 2026
Keywords : Cardiac imaging, Aortic stenosis, Frailty, Artificial intelligence
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The Author(s). Publié par Elsevier Masson SAS. Tous droits réservés.
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