Artificial intelligence demonstrates high potential to improve the screening and diagnosis of cardiac amyloidosis when applied to the analysis of clinical and imaging tests.
Does artificial intelligence improve the screening and diagnosis of cardiac amyloidosis?
Artificial intelligence applied to clinical data, ECG, and cardiovascular imaging demonstrates high diagnostic accuracy for cardiac amyloidosis, offering a promising strategy for early detection and screening.
Fundamento: Cardiac amyloidosis is a rare form of infiltrative cardiomyopathy characterized by the deposition of proteins in the myocardium, resulting in increased wall thickness, impaired ventricular function, and possible progression to heart failure.Diagnosis is challenging due to the low prevalence of the disease and the nonspecific nature of its clinical manifestations.The application of artificial intelligence (AI) to the analysis of medical tests emerges as a promising strategy for early detection, more accurate diagnosis, and timely initiation of treatment.Methods: An integrative literature review was conducted on the use of AI in the diagnosis of cardiac amyloidosis.Articles published between 2019 and 2024 were searched in the PubMed, Scopus, Web of Science, Embase, and Cochrane Library databases.Results: Of the 420 articles initially identified, 21 met the eligibility criteria and were included in the final analysis.A predominance of retrospective observational studies applying machine learning models was observed.Among the diagnostic modalities evaluated in association with AI, electrocardiography and echocardiography were the most frequently studied tests. Conclusion: AI demonstrates high potential to improve the screening and diagnosis of cardiac amyloidosis whenapplied to the analysis of clinical and imaging tests.The findings of this review indicate that AI may accelerate the diagnostic process, reduce the need for invasive procedures, and optimize the use of health care resources.However, to expand its integration into clinical practice and enhance its generalizability, further model refinement and validation in more diverse populations are required.
Lemos et al. (Thu,) conducted a review in Cardiac Amyloidosis (n=21). Artificial Intelligence vs. Conventional diagnostic methods was evaluated on Diagnostic performance (AUC, sensitivity, specificity) of AI models. Artificial intelligence demonstrates high potential to improve the screening and diagnosis of cardiac amyloidosis when applied to the analysis of clinical and imaging tests.