Clemens P Spielvogel, David Haberl, Katharina Mascherbauer, Jing Ning, Kilian Kluge, Tatjana Traub-Weidinger, Rhodri H Davies, Iain Pierce, Kush Patel, Thomas Nakuz, Adelina Göllner, Dominik Amereller, Maria Starace, Alice Monaci, Michael Weber, Xiang Li, Alexander R Haug, Raffaella Calabretta, Xiaowei Ma, Min Zhao, Julia Mascherbauer, Andreas Kammerlander, Christian Hengstenberg, Leon J Menezes, Roberto Sciagra, Thomas A Treibel, Marcus Hacker, Christian Nitsche
BACKGROUND: The diagnosis of cardiac amyloidosis can be established non-invasively by scintigraphy using bone-avid tracers, but visual assessment is subjective and can lead to misdiagnosis. We aimed to develop and validate an artificial intelligence (AI) system for standardised and reliable screening of cardiac amyloidosis-suggestive uptake and assess its prognostic value, using a multinational database of 99m Tc-scintigraphy data across multiple tracers and scanners. METHODS: In this retrospective, international, multicentre, cross-tracer development and validation study, 16 241 patients with 19 401 scans were included from nine centres: one hospital in Austria (consecutive recruitment Jan 4, 2010, to Aug 19, 2020), five hospital sites in London, UK (consecutive recruitment Oct 1, 2014, to Sept 29, 2022), two centres in China (selected scans from Jan 1, 2021, to Oct 31, 2022), and one centre in Italy (selected scans from Jan 1, 2011, to May 23, 2023)...
April 2024: The Lancet. Digital health