An observational study in Endodontic Working Length Determination, sponsored by Alanya Alaaddin Keykubat University. Not yet recruiting. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-08-26.
Sponsored by Alanya Alaaddin Keykubat University · Observational
This study tests whether artificial intelligence (AI) can accurately measure the length of curved root canals from dental x-rays. Curved root canals are hard to measure correctly, and wrong measurements can lower the success of root canal treatment.
Adults over 18 years old who need root canal treatment can take part. Researchers will use x-rays taken during the patient's normal treatment. No extra x-rays, procedures, or visits are needed. An AI program will be trained to measure canal length automatically, and its measurements will be compared to measurements made by a human expert.
The results may show whether AI can measure root canal length faster and more consistently, which could help dentists plan treatment more accurately in the future.
Working length determination is a critical step in root canal treatment, and inaccurate measurement can lead to under- or over-instrumentation, post-operative pain, and reduced treatment success. This is particularly challenging in curved root canals, where the apical foramen often deviates from the anatomical or radiographic apex, and where small endodontic files are difficult to visualize on periapical radiographs. Conventional methods for working length determination, including tactile sensation, electronic apex locators, and radiographic interpretation, are subject to observer variability and technical limitations, especially as canal curvature increases.
Recent advances in artificial intelligence, particularly deep learning-based image analysis, have shown promise in improving the objectivity and consistency of measurements derived from dental radiographs. This study aims to develop and evaluate deep learning models (including convolutional neural network architectures such as GoogleNet Inception V3, U-Net, Mask R-CNN, and YOLO-based networks) for the automatic detection and measurement of curved root canal length on periapical radiographs, and to compare the performance of these models with measurements made by a human observer.
Periapical radiographs will be obtained using the paralleling technique during the working length confirmation stage of routine root canal treatment, as part of the patient's standard clinical care. No additional radiographic exposure, procedure, or clinical visit will be performed solely for research purposes. Radiographic images will be anonymized and labeled using canal length segmentation, with pixel-based measurements calibrated to millimeters based on DICOM metadata.
The dataset will be divided into training, validation, and test subsets. Model performance will be evaluated using standard classification and segmentation metrics, including sensitivity, precision, F1 score, Intersection over Union (IoU), and Receiver Operating Characteristic (ROC) curve analysis with area under the curve (AUC).
Alanya Alaaddin Keykubat University is the lead sponsor of 51 studies on the registry; 17 are open to participants now.
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The study population consists of adult patients (18 years and older) presenting for routine root canal treatment at the Department of Endodontics, Alanya Alaaddin Keykubat University Faculty of Dentistry, who have radiographically identified curved root canals. No sex-based restriction is applied. Periapical radiographs obtained during the working length confirmation stage of standard clinical care will be used; no additional radiographic exposure or procedure will be performed for research purposes.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Sensitivity of AI Model in Detecting Curved Root Canal Length
Agreement between the AI-predicted root canal length and the length determined by a human observer on periapical radiographs, assessed by sensitivity, calculated as TP/(TP+FN), where TP (true positive) represents the overlapping region between the ground truth and predicted segmentation, and FN (false negative) represents the region present in the ground truth but not in the prediction.
Time frame: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
Precision of AI Model in Detecting Curved Root Canal Length
Agreement between the AI-predicted root canal length and the length determined by a human observer on periapical radiographs, assessed by precision, calculated as TP/(TP+FP), where FP (false positive) represents the region present in the prediction but not in the ground truth.
Time frame: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
F1 Score of AI Model in Detecting Curved Root Canal Length
F1 score of the AI-predicted root canal segmentation compared to ground truth on periapical radiographs, calculated as 2TP/(2TP+FP+FN), representing the harmonic mean of precision and sensitivity and interpreting the overlap between the ground truth and predicted pixels.
Time frame: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
Intersection over Union (IoU) of AI Model in Detecting Curved Root Canal Length
Intersection over Union between the AI-predicted root canal segmentation and the ground truth segmentation on periapical radiographs, calculated as TP/(TP+FN+FP), representing the overlapping area between the predicted result and the ground truth segmentation area.
Time frame: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
No study locations are listed for this record.
Plan to share: No — Individual participant data (periapical radiographs and associated measurements) contain identifiable patient imaging information. No data-sharing infrastructure or protocol has been established for this study, and sharing was not addressed in the informed consent obtained from participants. Data may be made available upon reasonable request to the corresponding author, subject to institutional and ethics committee approval.
Start is estimated for Sep 2026; primary completion for Nov 2026, 2 months later.
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This study is not yet recruiting, as verified in Aug 2026. You cannot join it, but the record below documents what was studied.
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Alanya Alaaddin Keykubat University