"Echocardiography is key for screening cardiac amyloidosis, and AI pattern recognition models like Us2.ai can now detect it from a single apical four‑chamber view. In clinical practice, however, we consider many other variables from the clinical lab, such as demographic data, ECG, and imaging data, so we sought to integrate these variables into the AI model to better reflect real world decision making and enhance detection accuracy.” – Jeremy Slivnick Assistant Professor, Division of Cardiovascular Medicine, The University of Chicago Medicine
Artificial intelligence is reshaping the way cardiac amyloidosis is detected. A notable example is Us2.ai’s pattern recognition model, which can identify the disease from a single apical 4‑chamber echo view (AI‑PRM in this study). While promising, such models have traditionally been limited by their reliance on a single imaging modality.
Presented at the Late-Breaking Clinical Science: Cardiovascular Imaging and Artificial Intelligence session at ESC Congress 2025, this study overcomes that limitation by combining Us2.ai’s pattern recognition model with clinical and laboratory data (AI‑ECM in this study), to enhance diagnostic accuracy for cardiac amyloidosis.