Deep learning framework for connected health and intraoral diagnostics: advancing intraoral soft and hard tissue condition analysis

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

doi:10.1117/12.3001072

13 March 2024 Deep learning framework for connected health and intraoral diagnostics: advancing intraoral soft and hard tissue condition analysis

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

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Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 128570G (2024) https://doi.org/10.1117/12.3001072
Event: SPIE BiOS, 2024, San Francisco, California, United States

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Abstract

The development of a deep learning framework specifically designed for the analysis of intraoral soft and hard tissue conditions is presented in this paper, with a focus on remote healthcare and intraoral diagnostic applications. The framework Faster R-CNN ResNet-50 FPN was trained on a dataset comprising 4,173 anonymized images of teeth obtained from buccal, lingual, and occlusal surfaces of 7 subjects. Ground truth annotations were generated through manual labeling, encompassing tooth number and tooth segmentation. The deep learning framework was built using platforms and APIs within Amazon Web Services (AWS), including SageMaker, S3, and EC2. It leveraged their GPU systems to train and deploy the models. The framework demonstrated high accuracy in tooth identification and segmentation, achieving an accuracy exceeding 60% for tooth numbering. Another framework for detecting teeth shades was trained using 25,519 RGB and 25,519 LAB values from VITA Classical shades. It used a basic neural network leading to 85 % validation accuracy. By leveraging the power of Faster R-CNN and the scalability of AWS, the framework provides a robust solution for real-time analysis of intraoral images, facilitating timely detection and monitoring of oral health issues. The initial results provide accurate identification of tooth numbering and valuable insights into tooth shades. The results achieved by the deep learning framework demonstrates its potential as a tool for analyzing intraoral soft and hard tissue parameters such as tooth staining. It presents an opportunity to enhance accuracy and efficiency in connected health and intraoral diagnostics applications, ultimately advancing the field of oral health assessment.

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

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Vrinda Jain, Ananya Jana, Abmael H. Oliveira, Thomas T. Livecchi, Mark C. Pierce, and Hrebesh M. Subhash "Deep learning framework for connected health and intraoral diagnostics: advancing intraoral soft and hard tissue condition analysis", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570G (13 March 2024); https://doi.org/10.1117/12.3001072

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