Endodontic Working Length DeterminationAI-CURLNCT07787078
AI-Based Working Length Determination in Curved Root Canals
An observational study of Endodontic Working Length Determination, run by Alanya Alaaddin Keykubat University.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Endodontic Working Length Determination
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- 22 Sep 2026to 22 Dec 2026
- How long will I take part?
- 1 Day
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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
Who cannot join
- 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
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: 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.
Read the 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).
- Conditions
- Endodontic Working Length Determination
- Keywords
- Deep learning, Working Length Determination, Root Canal Curvature, Periapical Radiography
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Measured once, at the time of model testing following completion of radiograph collection (estimated 12 months from study initiation)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Aug 2026
- Study start22 Sep 2026 (estimated)
- Primary completion22 Nov 2026 (estimated)
- Study completion22 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Endodontic Working Length DeterminationStudies of the same condition, open and under way.
- 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
- Cross-sectional
- Enrolment
- 200 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Alanya Alaaddin Keykubat University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Hatice BÜYÜKÖZER ÖZKAN, Alanya Alaaddin Keykubat University
- Investigators
- Hatice Büyüközer Özkan, Associate Professor (Doç. Dr.), Principal investigator, Alanya Alaaddin Keykubat University, Faculty of Dentistry
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07787078
- Sponsor's protocol ID
- ALKU-EC-2026-64
- Acronym
- AI-CURL
- Status last verified
- Aug 2026
- First submitted
- 18 Aug 2026
- Last updated
- 26 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- 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.
Publications
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.
PubMed 29777175Schwendicke 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.
PubMed 32315260
Study at a glance
From the registry record, last updated by the sponsor on 26 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of the Effectiveness of Artificial Intelligence in Determining the Accurate Working Length of Curved Root Canals in Endodontic Treatment
- ClinicalTrials.gov ID
- NCT07787078
- Acronym / sponsor ID
- AI-CURL / ALKU-EC-2026-64
- Conditions
- Endodontic Working Length Determination
- Keywords
- Deep learning, Working Length Determination, Root Canal Curvature, Periapical Radiography
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Alanya Alaaddin Keykubat UniversityUniversity, hospital or other
- Study start
- 22 Sep 2026 (estimated)
- Primary completion
- 22 Nov 2026 (estimated)
- Study completion
- 22 Dec 2026 (estimated)
- First posted
- 26 Aug 2026 (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 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.
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 design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
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.
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.
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.
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 26 Aug 2026.
- Background: PubMed 29777175Hosny 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.
- Background: PubMed 32315260Schwendicke 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.
- Individual participant dataSharing planned: 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.
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.
Hatice Büyüközer Özkan, Associate Professor (Doç. Dr.) principal investigator
Alanya Alaaddin Keykubat University, Faculty of Dentistry
Questions about this trial
What is this clinical trial studying?
An observational study of Endodontic Working Length Determination, run by Alanya Alaaddin Keykubat 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 6 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 26 Aug 2026. 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 Hatice Büyüközer Özkan, Doç. Dr. (Associate Professor) (05309710252, hatice.ozkan@alanya.edu.tr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). The study lists 4 primary outcomes in all. 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 NCT07787078, which also lists 2 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 26 Aug 2026.