Caries,DentalNCT06667986
Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries
An observational study of Caries,Dental, run by Cairo University.
Trial 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. Read more about this study
- Who is it for?
- Ages 22 to 60 yearsHealthy volunteers can take part
- Conditions the study is for
- Caries,Dental
- What is being tested?
- artificial intelligence models (YOLO and Mask-RCNN)
- How many people
- 322 participants
- Where
- No sites listed yet
- Study dates
- 15 Nov 2024to 15 Feb 2026
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 22 to 60 years
- All sexes
- Healthy volunteers accepted
- Adult
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.
Who may be able to join
- Adult Patients Aged 22-60 Patient
- Males or females.
- Patients have proximal restorations.
- Co-operative patients who show interest in participating in the study.
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: 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.
Read the 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.
- Conditions
- Caries,Dental
- Being tested
- artificial intelligence models (YOLO and Mask-RCNN)
- Keywords
- Secondary caries, Artificial intelligence, Digital bitewing radiography, Diagnostic accuracy study.
- Medical subject headings
- Dental Caries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Oct 2024
- Study start15 Nov 2024 (estimated)
- Primary completion15 Nov 2025 (estimated)
- Study completion15 Feb 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Caries,DentalStudies of the same condition, open and under way.
- More trials run by Cairo UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 322 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Heba Tallah Mohamed Mansour, Cairo University
- Investigators
- Prof. Dr. Heba Hamza, professor, Study director, Professor of Conservative Dentistry Department, Faculty of Dentistry, Cairo UniversityDr. Rawda Hisham A. ElAziz, lecturer, Study director, Lecturer of Conservative Dentistry Department, Faculty of Dentistry, Cairo UniversityDr. Asmaa Ahmed Elsayed Osman, lecturer, Study director, Lecturer of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06667986
- Sponsor's protocol ID
- AI in detect dental caries
- Status last verified
- Oct 2024
- First submitted
- 30 Oct 2024
- Last updated
- 31 Oct 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 38342368Mohammad-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.
PubMed 35367318Mertens 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.
PubMed 34656656Chen 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.
PubMed 36215971
Study at a glance
From the registry record, last updated by the sponsor on 31 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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)
- ClinicalTrials.gov ID
- NCT06667986
- Acronym / sponsor ID
- AI in detect dental caries
- Conditions
- Caries,Dental
- Keywords
- Secondary caries, Artificial intelligence, Digital bitewing radiography, Diagnostic accuracy study.
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 322 participants (estimated)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- 15 Nov 2024 (estimated)
- Primary completion
- 15 Nov 2025 (estimated)
- Study completion
- 15 Feb 2026 (estimated)
- First posted
- 31 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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 design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 1 Year
Who may qualify
- Ages
- 22 to 60 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- 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.
Key inclusion criteria (4 listed)
- Adult Patients Aged 22-60 Patient
- Males or females.
- Patients have proximal restorations.
- Co-operative patients who show interest in participating in the study.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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 | Other | – |
Primary outcome
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Oct 2024.
- Results publication: PubMed 38342368Chaves 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.
- Results publication: PubMed 35367318Mohammad-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.
- Results publication: PubMed 34656656Mertens 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.
- Results publication: PubMed 36215971Chen 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Prof. Dr. Heba Hamza, professor study director
Professor of Conservative Dentistry Department, Faculty of Dentistry, Cairo University
Dr. Rawda Hisham A. ElAziz, lecturer study director
Lecturer of Conservative Dentistry Department, Faculty of Dentistry, Cairo University
Dr. Asmaa Ahmed Elsayed Osman, lecturer study director
Lecturer of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University
Questions about this trial
What is this clinical trial studying?
An observational study of Caries,Dental, run by Cairo University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 31 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Heba-Tullah mohamed mansour, master (01025457570, hebatullah.mansour@dentistry.cu.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06667986, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Oct 2024.