Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy

BACKGROUND Constrictive pericarditis (CP) is an uncommon but reversible cause of diastolic heart failure if appropriately identified and treated. However, its diagnosis remains a challenge for clinicians. Arti ficial intelligence may enhance the identification of CP. OBJECTIVES The authors proposed a deep learning approach based on transthoracic echocardiography to differentiate CP from restrictive cardiomyopathy. METHODS Patients with a con firmed diagnosis of CP and cardiac amyloidosis (CA) (as the representative disease of restrictive cardiomyopathy) at Mayo Clinic Rochester from January 2003 to December 2021 were identified to extract baseline demographics. The apical 4 -chamber view from transthoracic echocardiography studies was used as input data. The patients were split into a 60:20:20 ratio for training, validation, and held -out test sets of the ResNet50 deep learning model. The model performance (differentiating CP and CA) was evaluated in the test set with the area under the curve. GradCAM was used for model interpretation. RESULTS A total of 381 patients were identi fied, including 184 (48.3%) CP, and 197 (51.7%) CA cases. The mean age was 68.7 +/- 11.4 years, and 72.8% were male. ResNet50 had a performance with an area under the curve of 0.97 to differentiate the 2 -class classi fication task (CP vs CA). The GradCAM heatmap showed activation around the ventricular septal area. CONCLUSIONS With a standard apical 4 -chamber view, our artificial intelligence model provides a platform to facilitate the detection of CP, allowing for improved work flow efficiency and prompt referral for more advanced evaluation and intervention of CP. (J Am Coll Cardiol Img 2024;17:349 -360) (c) 2024 by the American College of Cardiology Foundation.