Artificial Intellegence Rivals Digital Bitewing in Detect Secondary Caries
ConditionCaries,Dental
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age22-60
SponsorCairo University
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Adult Patients Aged 22-60 Patient
Males or females.
Patients have proximal restorations.
Co-operative patients who show interest in participating in the study.
Disqualifiers
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 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.
Trial design
Design model
Other
Time perspective
Prospective
Treatments tested in this trial
artificial intelligence models (YOLO and Mask-RCNN)
Other interventionmachine learning model will used to detect secondary caries around restorations by comparing the results with digital bitewing radiography
Treatment groups
No treatment groups listed
Trial outcomes
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
Time frame
baselineLocations
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: