Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries

NCT06667986 | Not Yet Recruiting

• 1 other identifier: AI in detect dental caries
• Study Type: observational
• Target: 322
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

Secondary caries, Artificial intelligence, Digital bitewing radiography, Diagnostic accuracy study.

Outcome Measures

Primary Outcomes (1)

• two deep learning models, YOLO and Mask-RCNN, will be trained on this dataset to accurately detect and classify images showing signs of secondary caries

  models will detect the presence or absence of secondary caries around restorations

  baseline

Interventions

artificial intelligence models (YOLO and Mask-RCNN) OTHER

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

Eligibility Criteria

• Age: 22 Years - 60 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Patients attending the Conservative Department at Cairo University Dental Clinic who present with proximal restorations, show no signs or symptoms, demonstrate cooperation, and express interest in participating in the study will be considered eligible. Patients with orthodontic appliances or bridgework that could impact the quality of radiographic imaging will be excluded.

You may qualify if:

• Adult Patients Aged 22-60 Patient
• Males or females.
• Patients have proximal restorations.
• Co-operative patients who show interest in participating in the study.

You may not qualify if:

• Patients with orthodontic appliances, or bridge work that might interfere with evaluation
• Patients with no caries.
• Systematic disease that may affect participation.
• Patients not willing to be part of the study or ones who refuse to sign the informed consent.

Sponsors & Collaborators

• Cairo University: lead

Related Publications (4)

• Chaves ET, Vinayahalingam S, van Nistelrooij N, Xi T, Romero VHD, Flugge T, Saker H, Kim A, Lima GDS, Loomans B, Huysmans MC, Mendes FM, Cenci MS. Detection of caries around restorations on bitewings using deep learning. J Dent. 2024 Apr;143:104886. doi: 10.1016/j.jdent.2024.104886. Epub 2024 Feb 9.

  PMID: 38342368 RESULT

• Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, Rokhshad R, Nadimi M, Schwendicke F. Deep learning for caries detection: A systematic review. J Dent. 2022 Jul;122:104115. doi: 10.1016/j.jdent.2022.104115. Epub 2022 Mar 30.

  PMID: 35367318 RESULT

• Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021 Dec;115:103849. doi: 10.1016/j.jdent.2021.103849. Epub 2021 Oct 14.

  PMID: 34656656 RESULT

• Chen X, Guo J, Ye J, Zhang M, Liang Y. Detection of Proximal Caries Lesions on Bitewing Radiographs Using Deep Learning Method. Caries Res. 2022;56(5-6):455-463. doi: 10.1159/000527418. Epub 2022 Oct 10.

  PMID: 36215971 RESULT

MeSH Terms

Conditions

Dental Caries

Condition Hierarchy (Ancestors)

Tooth Demineralization; Tooth Diseases; Stomatognathic Diseases

Study Officials

• Prof. Dr. Heba Hamza, professor

  Professor of Conservative Dentistry Department, Faculty of Dentistry, Cairo University

  STUDY DIRECTOR

• Dr. Rawda Hisham A. ElAziz, lecturer

  Lecturer of Conservative Dentistry Department, Faculty of Dentistry, Cairo University

  STUDY DIRECTOR

• Dr. Asmaa Ahmed Elsayed Osman, lecturer

  Lecturer of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University

  STUDY DIRECTOR

Central Study Contacts

Heba-Tullah mohamed mansour, master

CONTACT

01025457570; hebatullah.mansour@dentistry.cu.edu.eg

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: general Practitioner at Health Administration, Faculty of Pharmacy, Cairo University

Study Record Dates

• First Submitted: October 30, 2024
• First Posted: October 31, 2024
• Study Start: November 15, 2024
• Primary Completion: November 15, 2025
• Study Completion: February 15, 2026
• Last Updated: October 31, 2024

Record last verified: 2024-10