AI-Based Working Length Determination in Curved Root Canals (AI-CURL)
Evaluation of the Effectiveness of Artificial Intelligence in Determining the Accurate Working Length of Curved Root Canals in Endodontic Treatment
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.
Study Overview
Status
Conditions
Detailed Description
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).
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Hatice Büyüközer Özkan, Doç. Dr. (Associate Professor)
- Phone Number: 05309710252
- Email: hatice.ozkan@alanya.edu.tr
Study Contact Backup
- Name: Tuğba Gök
- Phone Number: 05522521446
- Email: tugba.gok@alanya.edu.tr
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Eligibility Criteria
- Age 18 years or older
- Presenting for routine root canal treatment at Alanya Alaaddin Keykubat University Faculty of Dentistry
- Radiographically identified curved root canal(s)
- Periapical radiograph obtained using the paralleling technique during working length confirmation
- Willing and able to provide informed consent
Exclusion Criteria:
- Age under 18 years
- Root canals without curvature
- Periapical radiographs of insufficient diagnostic quality (e.g., distortion, poor image quality preventing accurate canal length assessment)
- Unwillingness to provide informed consent
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Sensitivity of AI Model in Detecting Curved Root Canal Length
Time Frame: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
|
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.
|
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
Time Frame: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
|
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.
|
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
Time Frame: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
|
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.
|
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
Time Frame: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
|
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.
|
Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
|
Collaborators and Investigators
Investigators
- Principal Investigator: Hatice Büyüközer Özkan, Associate Professor (Doç. Dr.), Alanya Alaaddin Keykubat University, Faculty of Dentistry
Publications and helpful links
General Publications
- Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020 Jul;99(7):769-774. doi: 10.1177/0022034520915714. Epub 2020 Apr 21.
- Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18(8):500-510. doi: 10.1038/s41568-018-0016-5.
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- ALKU-EC-2026-64
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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