ATTRACTnet was developed as an AI-based screening tool for transthyretin-mediated amyloid cardiomyopathy (ATTR-CM). In a real-world population, use of the ATTRACTnet score resulted in a higher diagnostic yield compared with regular screening.
This summary is based on the publication of Jain SS, Sun T, Pierson E, et al. - Detecting Transthyretin Cardiac Amyloidosis With Artificial Intelligence: A Nonrandomized Clinical Trial. JAMA Cardiol. 2026 Feb 1;11(2):117-124. doi: 10.1001/jamacardio.2025.4591
Introduction and methods
Background
Transthyretin-mediated amyloid cardiomyopathy (ATTR-CM) is a progressive and fatal disease characterized by the deposition of amyloid fibrils, which consist of misfolded transthyretin (TTR) aggregates, in the myocardium. As the initial symptoms and signs of ATTR-CM are nonspecific, early-stage ATTR-CM has historically been underdiagnosed [1,2], particularly in Black patients [3,4]. Given the rapid expansion of treatment options for ATTR-CM, early diagnosis is imperative to delay disease progression and improve patient outcomes [5-7]. However, traditional risk scores and AI models have not been used to prospectively assess the impact of AI-augmented diagnostic processes on improving the detection of ATTR-CM in a real-world setting [8-10].