AI enabled panoramic radiographs improve dental diagnostics and identify precise dental anamoly
Automatic segmentation of teeth is crucial for diagnosing tooth structures, damages, and proposing the best dental treatments. Panoramic radiographs use ionizing radiation with a limited dose to capture a large area of the maxilla and mandible in a single projection.
Artificial intelligence enabled panoramic radiographs improve dental diagnostics and identify precise dental anamoly suggests a new study published in the Journal of Dentistry.
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.
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