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Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries

NCT ID: NCT06667986

Last Updated: 2024-10-31

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

322 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-11-15

Study Completion Date

2026-02-15

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Caries,Dental

Keywords

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

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Interventions

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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

Intervention Type OTHER

Eligibility Criteria

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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.
Minimum Eligible Age

22 Years

Maximum Eligible Age

60 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Cairo University

OTHER

Sponsor Role lead

Responsible Party

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Heba Tallah Mohamed Mansour

general Practitioner at Health Administration, Faculty of Pharmacy, Cairo University

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Prof. Dr. Heba Hamza, professor

Role: STUDY_DIRECTOR

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

Dr. Rawda Hisham A. ElAziz, lecturer

Role: STUDY_DIRECTOR

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

Dr. Asmaa Ahmed Elsayed Osman, lecturer

Role: STUDY_DIRECTOR

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

Central Contacts

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Heba-Tullah mohamed mansour, master

Role: CONTACT

Phone: 01025457570

Email: [email protected]

References

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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.

Reference Type RESULT
PMID: 38342368 (View on PubMed)

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.

Reference Type RESULT
PMID: 35367318 (View on PubMed)

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.

Reference Type RESULT
PMID: 34656656 (View on PubMed)

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.

Reference Type RESULT
PMID: 36215971 (View on PubMed)

Other Identifiers

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AI in detect dental caries

Identifier Type: -

Identifier Source: org_study_id