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JACC-Cardiovasc. Imag., Oct 25 2023, S1936-878X(23)00437-0 | https://doi.org/10.1016/j.jcmg.2023.09.011 Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy

Chieh-Ju Chao MD ^a,b, Jiwoong Jeong MS ^b,c, Reza Arsanjani MD ^b, Kihong Kim MD, PhD ^a, Yi-Lin Tsai MD ^d, Wen-Chung Yu MD ^d, Juan M. Farina MD ^b, Ahmed K. Mahmoud MD ^b, Chadi Ayoub MD, PhD ^b, Martha Grogan MD^a, Garvan C. Kane MD, PhD ^a, Imon Banerjee PhD^b,c, Jae K. Oh MD^a

^aMayo Clinic Rochester, Rochester, Minnesota, USA

^bMayo Clinic Arizona, Scottsdale, Arizona, USA

^cArizona State University, Tempe, Arizona, USA

^dTaipei Veterans General Hospital, Taipei, Taiwan

Address for correspondence: Dr Jae K. Oh

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.

논문정보

형식Research article
게재일2023년 10월 (BRIC 등록일 2024-01-09)
연구진국외 연구진
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