The goal of this observational study is to compare the diagnostic accuracy of Clinical examination as a standard for detection, identification and scoring of White Spot Lesions Versus Artificial intelligence analysis of intraoral photographs. The photographs are examined by experienced dental professionals to maintain diagnostic accuracy. Machine learning models YOLO and Mask-RCNN will analyze these images in three phases: pre-analytical, analytical and post-analytical. A dataset of 329 labelled photographs, annotated by experts, is used to train these models. Data augmentation methods enhance model performance, and accuracy is assessed against clinical examination results to confirm reliability.
The main question it aims to answer is:
\- Is artificial intelligence analysis of intraoral photographs as accurate as clinical assessment in the detection, identification, and scoring of white spot lesions among adult Egyptian patients attending Cairo University Dental Hospital?
Adult patients aged 20 - 60 years
Males or Females
Patients with white spot lesions of teeth 4 - Co-operative patients with interest in participation in the study
Patients with orthodontic appliances or bridgework that might interfere with evaluation and assessment
Patients with no white spot lesions
Patients with systematic diseases that might affect participation
Patients refusing to sign the informed consent or not willing to be part of the study