Utilizing artificial intelligence to detect cardiac amyloidosis in patients with severe aortic stenosis: A step forward to diagnose the underdiagnosed.

Muller, Steven A; Hauptmann, Laurenz; Nitsche, Christian; Oerlemans, Marish IFJ

The article discusses the use of artificial intelligence to detect cardiac amyloidosis in patients with severe aortic stenosis, a condition historically underdiagnosed due to lack of treatment options. The study by Shiri et al. in the European Journal of Nuclear Medicine and Molecular Imaging focused on utilizing machine-learning algorithms to identify ATTR-CM in patients undergoing transcatheter aortic valve implantation. The research found that CT strain showed the best diagnostic performance, potentially serving as a screening tool for concomitant ATTR-CM in patients with severe AS. Further studies are needed to validate the model and determine its feasibility in clinical practice, as well as to investigate the effects of ATTR-specific medication in patients with dual AS and ATTR-CM.

HEART valve prosthesis implantation; RADIONUCLIDE imaging; DISEASE risk factors; DELAYED diagnosis; HEART failure patients; ARRHYTHMIA

European Journal of Nuclear Medicine & Molecular Imaging, 2025, Vol 52, Issue 2, p482

1619-7070

Academic Journal

10.1007/s00259-024-06928-y