IsmailSebz/SW-AI-51-Video-Embedding-Powered-Large-Vision-Models-for-Dental-Disease-Detection-in-Uganda

Rep for AI group project # SW-AI-51: Video Embedding Powered Large Vision Models for Dental Disease Detection in Uganda ## Overview This project, **SW-AI-51**, explores the use of **AI-powered vision models** for **automatic detection of dental diseases** in patients through **video embeddings and large vision models (LVMs)**. It is designed to assist dental professionals in Uganda by providing an **AI-assisted diagnostic tool** capable of analyzing intraoral videos and images to identify potential dental conditions. The system leverages **deep learning**, **computer vision**, and **embedding techniques** to extract meaningful features from dental imagery and predict specific oral health issues efficiently. --- ## Keywords - Large Vision Models (LVMs), - Dentistry - Video Embeddings - Dental Disease - Detection and Localization - Explainable AI - Resource-Constrained Screening --- ## Objectives - To develop a **large vision model** capable of identifying various **dental diseases** from video input. - To use **video embedding representations** for accurate feature extraction and disease classification. - To provide a **scalable and accessible solution** for dental diagnostics in **resource-limited healthcare settings**. - To support **local dental practitioners in Uganda** with AI-driven insights for early detection and improved treatment planning. --- ## Features - **AI-based disease detection** from dental videos and images. - **Video embedding pipeline** for frame-level feature extraction. - **Transfer learning** using state-of-the-art large vision models (e.g., ViT, CLIP, or YOLO variants). - **Prediction and visualization dashboard** for dental conditions. - **Localized for Uganda** – trained on regional datasets for improved accuracy on local cases. --- ## System Architecture 1. **Data Acquisition** – Intraoral videos and images are collected from dental clinics. 2. **Preprocessing** – Frames are extracted, cleaned, and normalized for analysis. 3. **Embedding Generation** – …