Dr. Ammar Mohammed
Eng. Nour EL-Huda
Periodontal disease (PD) is characterized by gingival inflammation, connective tissue loss, or alveolar bone degradation. It’s the world’s sixth most common inflammatory disease. Periodontal disease is the leading cause of tooth loss. Clinical examination and radiograph analysis are two of the most popular approaches used nowadays to diagnose Periodontal disease. Due to the constraints of the standard diagnostic paradigm, inaccuracy due to human error, and conflicting judgements by various examiners. As a result, more consistent method of identifying PD is required. Because of advancements in machine learning research, automated medical support systems are in high demand for detecting periodontitis, and early detection may postpone the beginning of tooth loss. The key concept of our proposed system is to diagnose periodontal disease from X-ray images using Deep Learning algorithms. The computer-assisted system will determine whether the image is periodontal or not, and by applying fuzzy rules the system will be able to detect the severity as well as the grade of the case.
The goal of this study is to use panoramic radiography to assist dentists in predicting the severity of disease. Teeth numbers for identified lesions should be provided to make the system more clinically applicable in dental practice. Reduce the clinical diagnosis procedures for chronic periodontitis.
Our goal is to promote software development by giving a fully described system design. Not only is there a specified procedure for designing and developing the system, but there are also critical specifics for the program and system being produced.
Documents and Presentations
You will find here the documents and presentation for our proposal.
You will find here the documents and presentation for our SRS.
You will find here the documents and presentation for our SDD.
You will find here the documents and presentation for our Thesis
CNN-based Approach for Prediction of Periodontally Teeth
Dell Envision the Future 2022
We participated in this compitition and reached the top 100 projects selected around the world and and finished as one of the top 100 projects.