Affordable intraoral camera integrated with deep learning for at-home monitoring and detection of oral soft and hard tissue conditions

Vrinda Jain; Ananya Jana; Abmael H. Oliveira; Thomas T. Livecchi; Steven L. Jacques; Mark C. Pierce; Hrebesh M. Subhash

doi:10.1117/12.3000749

12 March 2024 Affordable intraoral camera integrated with deep learning for at-home monitoring and detection of oral soft and hard tissue conditions

Vrinda Jain, Ananya Jana, Abmael H. Oliveira, Thomas T. Livecchi, Steven L. Jacques, Mark C. Pierce, Hrebesh M. Subhash

Author Affiliations +

Proceedings Volume 12832, Optics and Biophotonics in Low-Resource Settings X; 1283209 (2024) https://doi.org/10.1117/12.3000749
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

To address an increasing demand for accessible and affordable tools for at-home oral health assessment, this paper presents the development of a low-cost intraoral camera integrated with a deep learning approach for image analysis. The camera captures and analyzes images of soft and hard oral tissues, enabling real-time feedback on potential tooth staining and empowering users to proactively manage their oral health. The system utilizes an Azdent intraoral USB camera with the Raspberry Pi 400 computer and Intel® Neural Computing Stick for real-time image acquisition and processing. A neural network was trained on a dataset comprising 102,062 CIELAB and RGB values from the VITA classical shade guide. Ground truth annotations were generated through manual labeling, encompassing tooth number and stain levels. The deep learning approach demonstrated high accuracy in tooth stain identification with a testing accuracy exceeding 0.6. This study demonstrates the capacity of low-cost camera hardware and deep learning algorithms to effectively categorize tooth stain levels with high accuracy. By bridging the gap between professional care and homebased oral health monitoring, the development of this low-cost platform holds promise in facilitating early detection and monitoring of oral health issues.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Vrinda Jain, Ananya Jana, Abmael H. Oliveira, Thomas T. Livecchi, Steven L. Jacques, Mark C. Pierce, and Hrebesh M. Subhash "Affordable intraoral camera integrated with deep learning for at-home monitoring and detection of oral soft and hard tissue conditions", Proc. SPIE 12832, Optics and Biophotonics in Low-Resource Settings X, 1283209 (12 March 2024); https://doi.org/10.1117/12.3000749

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