Toward development of automated grading system for carious lesions classification using deep learning and OCT imaging

Hassan S. Salehi; Majd Barchini; Qingguang Chen; Mina Mahdian

doi:10.1117/12.2581318

15 February 2021 Toward development of automated grading system for carious lesions classification using deep learning and OCT imaging

Hassan S. Salehi, Majd Barchini, Qingguang Chen, Mina Mahdian

Author Affiliations +

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1160014 (2021) https://doi.org/10.1117/12.2581318
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Dental caries remains the most prevalent chronic disease in both children and adults. Optical coherence tomography (OCT) is a noninvasive optical imaging modality utilized to image oral samples to diagnose carious lesions, but detecting early stage dental caries with high-level accuracy remains challenging. Deep learning models have been employed to classify OCT images for various healthcare applications. In this paper, human tooth specimens were imaged ex vivo using OCT imaging systems, and a three-class grading system based on deep learning model for detection and classification of carious lesions was developed. This study is a step forward in the development of automated deep learning/OCT imaging system for early dental caries diagnosis.

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Hassan S. Salehi, Majd Barchini, Qingguang Chen, and Mina Mahdian "Toward development of automated grading system for carious lesions classification using deep learning and OCT imaging", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1160014 (15 February 2021); https://doi.org/10.1117/12.2581318

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