In vivo detection of plaque erosion by intravascular optical coherence tomography using artificial intelligence

Plaque erosion is one of the most common underlying mechanisms for acute coronary syndrome (ACS). Optical coherence tomography (OCT) allows in vivo diagnosis of plaque erosion. However, challenge remains due to high inter- and infra-observer variability. We developed an artificial intelligence method based on deep learning for fully automated detection of plaque erosion in vivo, which achieved a recall of 0.800 +/- 0.175, a precision of 0.734 +/- 0.254, and an area under the precision-recall curve (AUC) of 0.707. Our proposed method is in good agreement with physicians, and can help improve the clinical diagnosis of plaque erosion and develop individualized treatment strategies for optimal management of ACS patients. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

In this study, we developed an artificial intelligence method based on deep learning for automated detection of plaque erosion in vivo, which showed good agreement with physicians and can help improve the clinical diagnosis of plaque erosion and develop individualized treatment strategies for ACS patients.