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Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries

NCT06667986 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 322

Last updated 2024-10-31

No results posted yet for this study

Summary

This study uses digital bitewing radiography as a standard for diagnosing proximal secondary caries. Patients will undergo imaging with a parallel technique and fixed settings to ensure high-quality, consistent images. Radiographs are interpreted 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 322 labeled images, annotated by experts, is used to train these models. Data augmentation methods enhance model performance, and accuracy is assessed against radiographic results to confirm reliability.

Conditions

  • Caries,Dental

Interventions

OTHER

artificial intelligence models (YOLO and Mask-RCNN)

machine learning model will used to detect secondary caries around restorations by comparing the results with digital bitewing radiography

Sponsors & Collaborators

  • Cairo University

    lead OTHER

Principal Investigators

  • Prof. Dr. Heba Hamza, professor · Professor of Conservative Dentistry Department, Faculty of Dentistry, Cairo University

  • Dr. Rawda Hisham A. ElAziz, lecturer · Lecturer of Conservative Dentistry Department, Faculty of Dentistry, Cairo University

  • Dr. Asmaa Ahmed Elsayed Osman, lecturer · Lecturer of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University

Eligibility

Min Age
22 Years
Max Age
60 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-11-15
Primary Completion
2025-11-15
Completion
2026-02-15

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06667986 on ClinicalTrials.gov