Endodontic RetreatmentAi RetreatmentNCT07611279
Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs
A study testing Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs in people with Endodontic Retreatment and Non-surgical Retreatment, run by Cairo University.
Trial summary
The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Endodontic Retreatment, Non-surgical Retreatment, Endodontics
- What is being tested?
- Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 123 participants
- Where
- No sites listed yet
- Study dates
- Jul 2026to Jan 2027
- Phase
- Interventional
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Periapical radiographs of maxillary and mandibular molars requiring non-surgical endodontic retreatment will be included. Radiographs should exhibit satisfactory image quality, characterized by adequate sharpness, contrast, and minimal distortion or noise to allow accurate assessment of relevant anatomical and treatment-related features. Images should clearly display the tooth of interest, surrounding periapical structures, and any existing root canal filling materials or restorations.
Who cannot join
- Deciduous teeth, non-restorable, non-treated teeth
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
The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.
- Conditions
- Endodontic Retreatment, Non-surgical Retreatment, Endodontics, AI (Artificial Intelligence), Deep Learning Model, DIFFICULTY ASSESSMENT
- Being tested
- Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs
- Keywords
- endodontic retreatment, difficulty assessment, endodontics, ai, artificial intelligence, deep learning model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical RadiographsDiagnostic test
Also called Deep learning model, CNN model, AI model
This study will employ a retrospective diagnostic accuracy design focused on the development and validation of a deep learning-based model for automated prediction of endodontic retreatment difficulty in maxillary and mandibular molars using periapical radiographs. The methodology will involve radiographic data acquisition, expert annotation of case difficulty according to standardized criteria, deep learning model development and training, and comprehensive performance evaluation of the proposed system.
| Study group | Role | What they receive |
|---|---|---|
| Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs | Experimental | This study will employ a retrospective diagnostic accuracy design focused on the development and validation of a deep learning-based model for automated prediction of endodontic retreatment difficulty in maxillary and mandibular molars using periapical radiographs. The methodology will involve radiographic data acquisition, expert annotation of case difficulty according to standardized criteria, deep learning model development and training, and comprehensive performance evaluation of the proposed system. Receives: Diagnostic Test: Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs |
What is being measured
The primary outcome is the main question the study is designed to answer.
diagnostic accuracy
Diagnostic performance of the deep learning model in predicting endodontic retreatment difficulty level
Measured: From Data collection to model testing up to 60 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 May 2026
- Study startJul 2026 (estimated)
- Primary completionJan 2027 (estimated)
- Study completionJan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Endodontic RetreatmentStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 123 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Noha Mohamed Elsaber, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07611279
- Sponsor's protocol ID
- newendo7.1.1
- Acronym
- Ai Retreatment
- Status last verified
- May 2026
- First submitted
- 14 May 2026
- Last updated
- 28 May 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 28 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs
- ClinicalTrials.gov ID
- NCT07611279
- Acronym / sponsor ID
- Ai Retreatment / newendo7.1.1
- Conditions
- Endodontic Retreatment, Non-surgical Retreatment, Endodontics, AI (Artificial Intelligence), Deep Learning Model, DIFFICULTY ASSESSMENT
- Keywords
- endodontic retreatment, difficulty assessment, endodontics, ai, artificial intelligence, deep learning model
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 123 participants (estimated)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- Jul 2026 (estimated)
- Primary completion
- Jan 2027 (estimated)
- Study completion
- Jan 2027 (estimated)
- First posted
- 28 May 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.
The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs | Experimental | Diagnostic Test: Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical RadiographsThis study will employ a retrospective diagnostic accuracy design focused on the development and validation of a deep learning-based model for automated prediction of endodontic retreatment difficulty in maxillary and mandibular molars using periapical radiographs. The methodology will involve radiographic data acquisition, expert annotation of case difficulty according to standardized criteria, deep learning model development and training, and comprehensive performance evaluation of the proposed system. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- Periapical radiographs of maxillary and mandibular molars requiring non-surgical endodontic retreatment will be included. Radiographs should exhibit satisfactory image quality, characterized by adequate sharpness, contrast, and minimal distortion or noise to allow accurate assessment of relevant anatomical and treatment-related features. Images should clearly display the tooth of interest, surrounding periapical structures, and any existing root canal filling materials or restorations.
Key exclusion criteria (1 listed)
- Deciduous teeth, non-restorable, non-treated teeth
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 |
|---|---|---|
| Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical RadiographsAlso: Deep learning model, CNN model, AI modelThis study will employ a retrospective diagnostic accuracy design focused on the development and validation of a deep learning-based model for automated prediction of endodontic retreatment difficulty in maxillary and mandibular molars using periapical radiographs. The methodology will involve radiographic data acquisition, expert annotation of case difficulty according to standardized criteria, deep learning model development and training, and comprehensive performance evaluation of the proposed system. | Diagnostic test | Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs |
Primary outcome
diagnostic accuracy
Time frame: From Data collection to model testing up to 60 weeks
Diagnostic performance of the deep learning model in predicting endodontic retreatment difficulty level
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 28 May 2026.
- Individual participant dataSharing planned: Undecided
Related trials for Endodontic Retreatment
Recruiting
Cryotherapy, PGE2, NGF Levels and Pain After Endodontic Retreatment
Asymptomatic Apical Periodontitis, Endodontic Retreatment
Recruiting
Modulation of Periapical Inflammation Following Nano-sized Intracanal Medication. A Randomized Controlled Clinical Trial
Post-operative Pain, Periapical Inflammation
Recruiting
The Effect of Different Intracanal Medicaments on Periapical Lesion Healing
Periapical Lesion Healing, Apical Periodontitis
Active, not recruiting
Effects of Irrigation Activation Systems on Postoperative Pain and Lesion Healing in Single-Visit Retreatment
Retreatment, Asymptomatic Apical Periodontitis
Recruiting
Evaluation of the Effect of Different Irrigation Activation Methods During Endodontic Retreatment
Endodontic Inflammation
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.
Questions about this trial
What is this clinical trial studying?
A study testing Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs in people with Endodontic Retreatment and Non-surgical Retreatment, 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 1 inclusion and 1 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 28 May 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 Noha El Saber, PhD student (+201157157197, nohaalsaber@dentistry.cu.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
diagnostic accuracy (time frame: From Data collection to model testing up to 60 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 28 May 2026.