AI enabled panoramic radiographs improve dental diagnostics and identify precise dental anamoly

This research focuses on performing teeth segmentation with panoramic radiograph images using a denoised encoder-based residual U-Net model, which enhances segmentation techniques and has the capacity to adapt to predictions with different and new data in the dataset, making the proposed model more robust and assisting in the accurate identification of damages in individual teeth.

The effective segmentation starts with pre-processing the Tufts dataset to resize images to avoid computational complexities. Subsequently, the prediction of the defect in teeth is performed with the denoised encoder block in the residual U-Net model, in which a modified identity block is provided in the encoder section for finer segmentation on specific regions in images, and features are identified optimally. The denoised block aids in handling noisy ground truth images effectively.

Results

Proposed module achieved greater values of mean dice and mean IoU with 98.90075 and 98.74147. Proposed AI enabled model permitted a precise approach to segment the teeth on Tuffs dental dataset in spite of the existence of densed dental filling and the kind of tooth.

The proposed model is pivotal for improved dental diagnostics, offering precise identification of dental anomalies. This could revolutionize clinical dental settings by facilitating more accurate treatments and safer examination processes with lower radiation exposure, thus enhancing overall patient care.

Reference:

Sultan A. Almalki, Shtwai Alsubai, Abdullah Alqahtani, Adel A. Alenazi,

Denoised encoder-based residual U-net for precise teeth image segmentation and damage prediction on panoramic radiographs, Journal of Dentistry, Volume 137, 2023, 104651,

ISSN 0300-5712, https://doi.org/10.1016/j.jdent.2023.104651

Keywords:

AI enabled, panoramic, radiographs, improve, dental, diagnostics, identify, precise, dental anamoly, Journal of Dentistry,Tooth segmentation; Damage prediction; Dental imaging; Panoramic radiographs; Residual U-Net; Hausdorff distance; Machine learning in dentistry