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Classification and segmentation of periodontal cystfor digital dental diagnosis using deep learning

Lakshmi T. K., Dheeba J.

Computer Assisted Methods in Engineering and Science

2023

Vol. 30, no. 2

131--149

The digital revolution is changing every aspect of life by simulating the ways humansthink, learn and make decisions. Dentistry is one of the major fields where subsets ofartificial intelligence are extensively used for disease predictions. Periodontitis, the mostprevalent oral disease, is the main focus of this study. We propose methods for classifyingand segmenting periodontal cysts on dental radiographs using CNN, VGG16, and U-Net.Accuracy of 77.78% is obtained using CNN, and enhanced accuracy of 98.48% is obtainedthrough transfer learning with VGG16. The U-Net model also gives encouraging results.This study presents promising results, and in the future, the work can be extended withother pre-trained models and compared. Researchers working in this field can develop novelmethods and approaches to support dental practitioners and periodontists in decision-making and diagnosis and use artificial intelligence to bridge the gap between humansand machines.

An automatic and effective tooth isolation method for dental radiographs

Lin P. L., Huang P. W., Cho Y. S., Kuo C. H.

Opto-Electronics Review

2013

Vol. 21, No. 1

126-136

Tooth isolation is a very important step for both computer-aided dental diagnosis and automatic dental identification systems, because it will directly affect the accuracy of feature extraction and, thereby, the final results of both types of systems. This paper presents an effective and fully automatic tooth isolation method for dental X-ray images, which contains up-per-lower jaw separation, single tooth isolation, over-segmentation verification, and under-segmentation detection. The upper-lower jaw separation mechanism is based on a gray-scale integral projection to avoid possible information loss and incorporates with the angle adjustment to handle skewed images. In a single tooth isolation, an adaptive windowing scheme for locating gap valleys is proposed to improve the accuracy. In over-segmentation, an isolation-curve verification scheme is proposed to remove excessive curves; and in under-segmentation, a missing-teeth detection scheme is proposed. The experimental results demonstrate that our method achieves the accuracy rates of 95.63% and 98.71% for the upper and lower jaw images, respectively, from the test database of 60 bitewing dental radiographs, and performs better for images with severe teeth occlusion, excessive dental works, and uneven illumination than that of Nomir and Abdel-Mottaleb's method. The method without upper-lower jaw separation step also works well for panoramic and periapical images.