Evaluation of 3D Modeling Workflows Using Dental CBCT Data for Periodontal Regenerative Treatment

Principal Investigator

This research aims to develop a novel deep-learning segmentation model for segmenting alveolar bone in periodontitis patients using Cone Beam Computed Tomography (CBCT) images. CBCT images of patients with periodontal bone loss will be obtained from the archive of the Maxillofacial Imaging unit, ODC Healthcare, Dhaka, Bangladesh. The dataset will be divided into three groups: training, testing, and validation. 70% of the data will be used to train the deep learning algorithm, ensuring adequate model training to segment all types of defective alveolar bone due to periodontitis. Subsequently, 20% of the dataset will be used to test the model's accuracy against the most advanced existing deep learning segmentation models. The remaining 10% will be used for independent validation of the model’s performance.

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