Artificial intelligence to assist the echocardiographic identification of transthyretin cardiac amyloidosis

Abstract Introduction Transthyretin cardiac amyloidosis (ATTR-CM) is a severe disease, and treatments are most effective when initiated at early stage. Echocardiography is the main tool for screening patients with suspected ATTR-CM or populations at high risk of ATTR-CM. Suspicion of ATTR-CM triggers a dedicated confirmatory evaluation. Echocardiographic diagnosis is however difficult to implement in a non-expert cardiologist workflow. An algorithm to assist the identification of potential cases would be helpful to assist clinicians. Objective To develop an algorithm using artificial intelligence (AI) to assist the identification of potential ATTR-CM cases using routine transthoracic echocardiography. Methods In this non-interventional, monocentric case-control study, a dedicated database, comprising confirmed ATTR-CM patients and a control group, was built. We trained and validated a deep learning model based on Convolutional Neural Network from transthoracic echocardiography video loops. Results A total of 4394 patients were included in the database: 208 in the ATTR-CM group (76% males, mean age 69±10 years old, 90% hereditary ATTR-CM with NAC I score in 77%) and 4186 in the control group (59% male, mean age 62±16 years). After training the model to identify potential ATTR-CM, the ROC curve AUC was 0.97, accuracy was 94.7%, precision was 87.3%, and recall 84.4%. After selecting a subgroup of 130 pairs of matched ATTR-CM/controls based on age, sex, and wall thickness, the algorithm performance remained robust (AUC 0.99, accuracy 99.4%, precision 100%, recall 98.3%). Conclusion and Perspectives: Diagnosis of ATTR-CM by echocardiography can be assisted by an algorithm using AI. The algorithm remains effective in distinguishing ATTR-CM from other causes of left ventricular hypertrophy. External validation of this tool is underway.ROC curveAssisted classification