Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis
Isaac Shiri; Sebastian Balzer; Giovanni Baj; Benedikt Bernhard; Moritz Hundertmark; Adam Bakula; Masaaki Nakase; Daijiro Tomii · 2024 · European Journal of Nuclear Medicine and Molecular Imaging
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
PURPOSE: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequent concomitant condition in patients with severe aortic stenosis (AS), yet it often remains undetected. This study aims to comprehensively evaluate artificial intelligence-based models developed based on preprocedural and routinely collected data to detect ATTR-CM in patients with severe AS planned for transcatheter aortic valve implantation (TAVI). METHODS: Tc]-DPD) for the presence of ATTR-CM. Clinical, laboratory, electrocardiogram, echocardiography, invasive measurements, 4-dimensional cardiac CT (4D-CCT) strain data, and C
Abstract by Isaac Shiri; Sebastian Balzer; Giovanni Baj; Benedikt Bernhard; Moritz Hundertmark; Adam Bakula; Masaaki Nakase; Daijiro Tomii, European Journal of Nuclear Medicine and Molecular Imaging (2024) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1007/s00259-024-06922-4