Applications of Artificial Intelligence in Constrictive Pericarditis: A Short Literature Review.

Abstract

Purpose of review: Constrictive pericarditis (CP) is a potentially curable condition characterized by the thickening, scarring, and calcification of the pericardium. A comprehensive approach, including clinical evaluations and imaging techniques such as echocardiography, computed tomography, and magnetic resonance imaging, is essential for timely diagnosis and intervention to prevent chronic complications and enhance patient outcomes. However, the rarity of CP and the specialized expertise required present challenges in diagnosis.

Recent findings: Emerging artificial intelligence applications show promise in enhancing clinical decision-making and improving outcomes. Studies utilizing cognitive machine learning and deep learning algorithms (ResNet50) achieved an AUC above 0.95 in distinguishing CP from restrictive cardiomyopathy. However, generalization and interpretability issues remain, and the development of AI applications for CP is still nascent due to challenges in obtaining large, high-quality echocardiographic datasets. Future research should evaluate the effectiveness of these models in diverse clinical scenarios, employing comprehensive echocardiography, point-of-care ultrasound, and other modalities to improve CP detection, individualized risk assessment, and treatment planning, ultimately enhancing patient prognosis.