Perspectives on Artificial Intelligence Enabled Heart Sound Analysis for Cardiac Amyloidosis

Cardiac amyloidosis was long considered rare and untreatable, but both assumptions have given way, and the disease is now recognized as an underdiagnosed and increasingly treatable cause of heart failure. The available therapies work by slowing amyloid deposition, so their benefit depends on early diagnosis, yet the condition is still identified late and usually only after referral for specialized imaging. Artificial intelligence has started to shorten this path. Models built on the electrocardiogram and the echocardiogram detect amyloid infiltration with high accuracy and have reached external validation, while artificial-intelligence-based analysis of heart sound, the cheapest and most widely available cardiac signal, has not yet been directly evaluated in this disease. The physiological rationale for studying it is strong, because a ventricle stiffened by amyloid is expected to alter the low-frequency diastolic sounds that reflect ventricular compliance, although whether these changes are specific to amyloidosis remains unknown. This review brings together the acoustic manifestations of cardiac amyloidosis, the signal-processing and machine learning methods relevant to heart sounds, the clinical evidence across imaging and acoustic modalities, and the practical and equity questions that deployment raises. Heart sound analysis remains the most scalable and least explored opportunity in the field. We outline the mechanistic rationale, the available methods, the lessons from the more mature electrocardiographic and echocardiographic literature, and a set of minimum design requirements for a first disease-specific study.