An analysis regarding the article “Artificial intelligence-enhanced electrocardiogram for the diagnosis of cardiac amyloidosis: A systemic review and meta-analysis”

Abstract

Cardiac Amyloidosis (CA) occurs when misfolded proteins accumulate in the heart muscle, leading to restrictive cardiomyopathy and possibly escalating to heart failure, impaired conduction system function, and sudden cardiac arrest. It is a significant clinical challenge due to its high rates of underdiagnosis and misdiagnosis. Research indicates that about 35% of individuals with CA have been incorrectly diagnosed with other prevalent cardiovascular diseases. CA is differentiated into various subtypes depending on the protein involved in its pathogenesis, with Transthyretin Amyloidosis (ATTR) and Light Chain Amyloidosis (AL) being the most common. A key diagnostic challenge is the subtle clinical presentation of CA, which often resembles other heart conditions such as restrictive cardiomyopathy with left ventricular hypertrophy or hypertrophic obstructive cardiomyopathy (HOCM). While several diagnostic methods are available for CA, many are expensive, invasive, and typically used after an initial clinical suspicion. Non-invasive tests like electrocardiography (ECG) are accessible but often have lower sensitivity in detecting CA. Given the limited expertise in recognizing CA symptoms in primary care settings, there is an urgent need for systems that can aid in early detection. It is crucial to develop systems that equip primary care providers with the tools and knowledge to recognize the subtle signs of cardiac amyloidosis, thereby enhancing patient outcomes.