Artificial Intelligence Versus Clinical Examination in White Spot Lesions Detection, Identification, And Scoring

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Conditions studied: White Spot Lesion of Tooth

In brief

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?

Key facts

Study ID
NCT07639749
Run by
Cairo University
People needed
329
Starts
2026-07-01
Expected to finish
2027-11-01
Last updated by the study team
2026-06-10

Who can join

Age: 20 and older, up to 60. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Full record on ClinicalTrials.gov

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