Journal
BIOMEDICAL OPTICS EXPRESS
Volume 13, Issue 7, Pages 3922-3938Publisher
Optica Publishing Group
DOI: 10.1364/BOE.459623
Keywords
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Funding
- National Natural Science Foundation of China [62075033, 62135002, 61921002, 82061130223]
- Sichuan Science and Technology Program [2020YFS0076]
- Fundamental Research Funds for the Central Universities
- Newton Fund [NAF\R11\1015]
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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.
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
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