Abstract
PurposeCardiac amyloidosis (CA) is a highly underdiagnosed disease characterized by the accumulation of misfolded amyloid protein fragments in the heart, resulting in reduced heart functionality and myocardial stiffness. Artificial intelligence (AI) has garnered considerable interest as a potential tool for diagnosing cardiovascular diseases, including CA. This systematic review concentrates on the application of AI in the diagnosis of CA.MethodsA comprehensive systematic search was performed on the databases of PubMed, Embase, and Medline, to identify relevant studies. The screening process was conducted in two stages, using predetermined inclusion and exclusion criteria, and was carried out in a blinded manner. In cases where discrepancies arose, the reviewers discussed and resolved the issue through consensus.ResultsFollowing the screening process, a total of 10 studies were deemed eligible for inclusion in this review. These investigations evaluated the potential utility of AI models that analyzed routine laboratory data, medical records, ECG, transthoracic echocardiography, CMR, and WBS in the diagnosis of CA.ConclusionAI models have demonstrated utility as a diagnostic tool for CA, with comparable or in one case superior efficacy to that of expert cardiologists.
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