Dental Disease Detection

Dental disease detection research focuses on developing accurate and accessible methods for identifying various oral health issues, moving beyond traditional X-rays and clinical examinations. Current efforts leverage deep learning architectures like YOLO, Faster R-CNN, and EfficientDet, often applied to intraoral radiographs and even sonic toothbrush data, to automate detection of caries, periodontal disease, and other anomalies. These advancements aim to improve diagnostic accuracy, particularly in resource-limited settings, and enable earlier intervention through at-home self-assessment tools and mobile applications incorporating federated learning for enhanced data privacy. The ultimate goal is to improve oral health outcomes through more efficient and accessible diagnostic capabilities.