Application of machine learning on dental optical coherence tomography (Conference Presentation)

Mei-Ru Chen; Tien-Yu Hsiao; Yi-Ching Ho; Chia-Wei Sun

doi:10.1117/12.2508392

7 March 2019 Application of machine learning on dental optical coherence tomography (Conference Presentation)

Mei-Ru Chen, Tien-Yu Hsiao, Yi-Ching Ho, Chia-Wei Sun

Proceedings Volume 10857, Lasers in Dentistry XXV; 108570G (2019) https://doi.org/10.1117/12.2508392
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

In this study, we combined optical technology and machine learning to classify dental problems.We took totally 16 dental samples and 79 OCT images including 32 dental calculus(CA) images and 47 normal (HC) images. After image processing, we obtained optical attenuation coefficient, surface roughness and spectral information, and we put these features into two layer neural networks for training. We divided the data into training (24 CA / 37 HC = 61 total) and test (8 CA / 10 HC = 18 total) data, and the training data was checked with 10-fold cross validation to confirm no over-trained. The results showed that the model validity is 78%, and the test results have a sensitivity of 86%, specificity of 100%, and total accuracy of 94%.

Conference Presentation

Citation Download Citation

Mei-Ru Chen, Tien-Yu Hsiao, Yi-Ching Ho, and Chia-Wei Sun "Application of machine learning on dental optical coherence tomography (Conference Presentation)", Proc. SPIE 10857, Lasers in Dentistry XXV, 108570G (7 March 2019); https://doi.org/10.1117/12.2508392

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