MANDIBLE

The purpose of this retrospective observational study is to investigate whether mandibular canal dimensions and sublingual fossa morphology can serve as radiographic predictors of the mandibular lateral lingual canal (MLLC) using cone-beam computed tomography (CBCT). CBCT images of adults with unilateral MLLC will be retrospectively evaluated. Mandibular canal diameter, mental foramen diameter, and sublingual fossa depth will be compared between the side with the MLLC and the contralateral side. In addition, the location, diameter, and course of the MLLC will be analyzed to identify radiographic features associated with its presence and to improve preoperative assessment for mandibular surgical procedures.

The goal of this clinical trial is to evaluate whether an anatomical prefabricated healing abutment (AHA) improves tissue preservation and clinical outcomes following immediate implant placement after tooth extraction in adults. Although immediate implant placement is a well-established treatment for replacing missing teeth, healing can result in changes to the surrounding bone and soft tissues that may affect the esthetic appearance, function, and long-term stability of the implant-supported restoration. This study will compare an anatomical healing abutment, designed to mimic the natural tooth emergence profile and support peri-implant tissues during healing, with a conventional healing abutment. The primary objectives are to determine whether the AHA better preserves soft tissue contours and alveolar bone, improves implant positioning accuracy and tissue stability during healing, and differs from a conventional healing abutment in complications or adverse events. Eligible participants will be adults requiring extraction of a hopeless tooth in the posterior maxilla or mandible who are suitable candidates for immediate implant placement. Following tooth extraction and implant placement, participants will receive either an anatomical prefabricated healing abutment or a conventional healing abutment and will return for scheduled follow-up visits over approximately 12 months to assess healing, implant stability, and changes in the surrounding hard and soft tissues. Researchers will collect clinical measurements, digital intraoral scans, photographs, and radiographic images throughout the study. Additional research imaging will be used to generate three-dimensional models to measure tissue volume changes and implant position accuracy. Specialized software will compare changes between the two groups over time. Safety will be evaluated by monitoring participants for complications, discomfort, inflammation, implant-related problems, and other adverse events throughout follow-up. All procedures will be performed by trained dental professionals using established clinical protocols. The results may improve understanding of how healing abutment design influences tissue preservation after immediate implant placement and help optimize future implant therapy by supporting better functional, esthetic, and long-term outcomes.

This retrospective validity study evaluates the accuracy of artificial intelligence (AI) in determining gender from mandibular morphometric linear measurements. The study utilizes pre-existing Cone Beam Computed Tomography (CBCT) scans of adult Egyptian dental patients. After automatic segmentation of the mandible from these scans, a radiologist will manually perform measurements from certain anatomical points. These measurements will be the reference standard for the AI models. A three-dimensional deep learning model will be developed to perform two tasks: 1. To identify the anatomical points and make the specified linear measurements from these points on the segmented mandibles 2. To accurately predict gender based on these measurements. (Main Objective) The primary objective of this study is to evaluate the accuracy of machine learning algorithms in gender identification from linear morphometric measurements of the mandible. The known gender from patient records will serve as the reference standard. This study will assess the reliability of AI as an objective tool for gender determination for forensic purposes.