Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries

October 30, 2024 updated by: Heba Tallah Mohamed Mansour, Cairo University

AI Rivals Traditional Bite Wing Radiography in Detecting Proximal Secondary Caries in A Group of Egyptian Patients at Cairo University, Faculty OF Dentistry Hospital (Diagnostic Accuracy Study)

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Dental caries are chronic diseases that results in the destruction of the hard tooth tissues. It is a multifactorial condition that often goes undiagnosed, especially when it is hidden or in its initial stages. Detecting non-cavitated lesions is crucial for their early management. The standard visual-tactile inspection often fails to identify early lesions on hard-to-reach surfaces, such as proximal areas and beneath restorations. Detecting proximal caries early is crucial for implementing effective treatments and achieving optimal outcomes. A common supplementary method for detecting early lesions on proximal surfaces and assessing their extent is bitewing radiography. The routine diagnostic approach combines clinical examination with radiographic evaluation. To increase the detection rate of proximal secondary caries, experts recommend integrating visual and clinical examinations with bitewing radiography. Intraoral bitewing radiographs can be captured using either film or digital sensors, with preference for digital systems due to their benefits of reduced patient exposure, time savings, image enhancement, and ease of image storage, retrieval, and transmission. Although more sensitive for detecting early lesions than visual-tactile assessments, bitewing evaluations comes with significant variance between examiners and a considerable proportion of false-positive or false-negative detections. Recent literature has explored the use of artificial intelligence (AI), a field of computer science focused on developing machines capable of mimicking human cognitive abilities, as a diagnostic tool for detecting caries lesions using dental (digital radiographic) images. As AI technology advances, an increasing number of studies have examined the diagnostic performance of AI-based models, emphasizing the importance of creating reliable tools like AI to enhance the diagnostic process. Numerous studies have assessed the performance of AI models on diverse types of dental radiographs, with a significant focus on bitewing radiographs (BWR). AI has been used for various applications in oral and dental health, including the detection of dental caries, endodontic treatment and diagnosis, periodontal issues, and the detection of oral lesion pathology. A reference dataset of caries diagnoses from bitewing radiographs by different examiners created this benchmark which serves as a crucial tool for comparing the diagnostic performance of AI models against human examiners, emphasizing the potential improvements in accuracy and reliability that AI can bring to dental diagnostics.

Study Type

Observational

Enrollment (Estimated)

322

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

Yes

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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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
Time Frame: baseline
models will detect the presence or absence of secondary caries around restorations
baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Cairo University

Investigators

  • Study Director: 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

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

November 15, 2024

Primary Completion (Estimated)

November 15, 2025

Study Completion (Estimated)

February 15, 2026

Study Registration Dates

First Submitted

October 30, 2024

First Submitted That Met QC Criteria

October 30, 2024

First Posted (Actual)

October 31, 2024

Study Record Updates

Last Update Posted (Actual)

October 31, 2024

Last Update Submitted That Met QC Criteria

October 30, 2024

Last Verified

October 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI in detect dental caries

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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