Denoised encoder-based residual U-net for precise teeth image segmentation and damage prediction on panoramic radiographs.

Objectives: 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.

Methods: 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.

Knowledge, Attitude, and Management of General Dentist toward Medication-related Osteonecrosis of the Jaws.

Background: Medication-related osteonecrosis of the jaw (MRONJ) is an intense negative drug response causing increasing bone destruction in the maxillofacial area of patients.

Aims And Objectives: To evaluate the knowledge and attitude of dental practitioner regarding risk factors of MRONJ in Saudi Arabia.

Materials And Methods: A cross-sectional, questionnaire survey was carried out in King Khalid Hospital, Al-Kharj among dental practioners.