Chao CJ, Jeong J, Arsanjani R, Kim K, Tsai YL, Yu WC, Farina JM, Mahmoud AK, Ayoub C, Grogan M, Kane GC, Banerjee I, Oh JK. Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy.
JACC Cardiovasc Imaging 2024;
17:349-360. [PMID:
37943236 DOI:
10.1016/j.jcmg.2023.09.011]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 09/07/2023] [Accepted: 09/25/2023] [Indexed: 11/10/2023]
Abstract
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. Artificial 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 confirmed 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 identified, 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 classification 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 workflow efficiency and prompt referral for more advanced evaluation and intervention of CP.
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