AI medical trial: AI echocardiography to detect cardiac amyloidosis — a clinical trial (ClinicalTrials.gov)

Cardiac amyloidosis is a disease in which misfolded protein is deposited in the heart muscle, causing mainly heart-failure symptoms. It often produces heart failure even though the left ventricle's pumping function (ejection fraction) is preserved, and it shows certain features on echocardiography such as thickened (hypertrophied) walls. Because it can resemble other heart conditions, identifying it with currently available echocardiographic tools remains challenging. Early diagnosis matters greatly, since it can connect patients to therapies that have improved survival.

This study (NCT07529860) is a prospective study testing how accurately an AI algorithm applied to echocardiographic images (the software Us2.ai) can pick up patients with findings suggestive of cardiac amyloidosis, especially the transthyretin (ATTR) type. Crucially, it does not treat the AI's output as the answer: accuracy is judged against methods that actually establish the diagnosis — bone-tracer SPECT (using 99mTc-pyrophosphate, PYP), cardiac MRI, and blood tests, with tissue confirmation if needed — used as ground truth. Echocardiography is the first-choice test for patients with breathlessness or suspected heart failure, and for those with risk factors such as hypertension and diabetes. Layering AI onto these routine scans could widen the chance of catching the disease earlier.

The study also aims to clarify the true prevalence of ATTR cardiac amyloidosis among patients referred for echocardiography who present red-flag features. Combining an existing AI tool with the first-line test and validating it against confirmatory methods is one example of how AI might be positioned for the early detection of an easily missed disease. Note that the accuracy of the approach is what this study sets out to evaluate; it is not established.