Abstract 10310: Deep Learning for a Diagnosis of Plaque Erosion in Patients with Acute Coronary Syndromes

Abstract

Background: The diagnosis of plaque erosion by optical coherence tomography (OCT) in patients with acute coronary syndromes (ACS) requires expertise in image interpretation. Objectives: This study sought to develop a deep learning (DL) algorithm that enables an accurate diagnosis of plaque erosion even for readers without experience in image interpretation. Methods: An image sequence transformer DL algorithm that mirrors the attention given by cardiologists to sequential images during a pull-back, was developed. The DL model was trained and internally validated for a diagnosis of plaque erosion both at the frame-level and at the patient-level in 581 ACS patients from 8 institutions in 4 countries. The model was externally tested in 292 ACS patients from an independent dataset. Training and validation dataset included 237,021 cross-sectional OCT images and testing dataset 65,394 cross-sectional OCT images. The OCT image classification performance was evaluated by area under the receiver-operating characteristic curve (AUC), and sensitivity and specificity. The results were compared against diagnoses made by OCT experts both at the frame-level and at the patient-level. Results: In the training and validation data set, 206 (43.2%) patients had plaque erosion, while in the external testing data set, 86 (29.5%) patients had plaque erosion. In the external testing data set, the DL model diagnosed plaque erosion with an AUC of 0.963 (95% CI, 0.962-0.963), a sensitivity of 89.9% (95% CI, 89.7-90.0), and a specificity of 91.1% (95% CI, 91.1-91.2) at the frame-level. At the patient-level, the DL model detected plaque erosion with an AUC of 0.901 (95% CI, 0.900-0.902), a sensitivity of 89.6% (95% CI, 89.4-89.8), and a specificity of 82.0% (95% CI, 81.9-82.2). Conclusion: A newly developed DL model enabled the accurate diagnosis of plaque erosion. This novel DL model will help cardiologists make an accurate diagnosis of plaque erosion in patients with ACS.