Endodontically Treated TeethNCT06450938
No Code Artificial Intelligence to Detect Radiographic Features Associated With Unsatisfactory Endodontic Treatment
A study testing AI guidance for finding radiographic features in people with Endodontically Treated Teeth and Endodontic Underfill, run by University of Copenhagen.
Trial summary
Developing neural network-based models for image analysis can be time-consuming, requiring dataset design and model training. No-code AI platforms allow users to annotate object features without coding. Corrective annotation, a "human-in-the-loop" approach, refines AI segmentations iteratively. Read more about this study
- Who is it for?
- Ages 20 to 40 yearsHealthy volunteers can take part
- Conditions the study is for
- Endodontically Treated Teeth, Endodontic Underfill, Endodontic Overfill
- What is being tested?
- AI guidance for finding radiographic features
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 80 participants
- Where
- No sites listed yet
- Study dates
- 30 Jul 2024to 13 Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 20 to 40 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- 1.Being a last year dental student at the university of Copenhagen
Who cannot join
- Having any previous AI-related experiences
- Not accepting to sign the informed consent
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Implementing a Corrective Annotation No Code Artificial Intelligence-based Software to Detect Several Radiographic Features Associated With Unsatisfactory Endodontic Treatment: A Randomized Controlled Trial
Developing neural network-based models for image analysis can be time-consuming, requiring dataset design and model training. No-code AI platforms allow users to annotate object features without coding. Corrective annotation, a "human-in-the-loop" approach, refines AI segmentations iteratively. Dentistry has seen success with no-code AI for segmenting dental restorations. This study aims to assess radiographic features related to root canal treatment quality using a "human-in-the-loop" approach.
Read the detailed description
The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potential in finding radiographic features and treatment planning in the field of cariology and endodontics. A growing body of literature suggests that DL models might assist dental practitioners in detecting radiographic features such as carious lesions, and periapical lesions, as well as predicting the risk of pulp exposure when doing caries excavation therapy. Although, the current literature lacks sufficient research on the interaction of participants and AI in an AI-based platform for detecting features associated with technical quality of endodontic treatment. This prospective randomized controlled trial aims to assess the performance of students when using an AI-based platform for detecting features associated with technical quality of endodontic treatment and predicting the long term prognosis of the treatment. The hypothesis is that participants' performance in the group with access to AI responses is similar to the control group without access to AI responses.
- Conditions
- Endodontically Treated Teeth, Endodontic Underfill, Endodontic Overfill, Apical Periodontitis
- Being tested
- AI guidance for finding radiographic features
- Keywords
- Artificial Intelligence, Endodontic treatment
- Medical subject headings
- Tooth, Nonvital, Periapical Periodontitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI guidance for finding radiographic featuresDevice
A secured website was made for the trial in which each student could log in using the assigned number. All the image datasets were uploaded to this website. The students will be randomly assigned to the experiment and control group. Both students were asked to segment the features associated with the quality of root canal treatment and predict the prognosis of treatment while the experiment group had access to AI guidance and the control group didn't.
| Study group | Role | What they receive |
|---|---|---|
| participants using guidance from artificial Intelligence | Experimental | the experimental arm refers to the group of participants who have access to the AI-based platform for detecting features associated with the technical quality of endodontic treatment. These participants will utilize the AI assistance during the study. Receives: Device: AI guidance for finding radiographic features |
| Control arm without any guidance from artificial Intelligence | No intervention | the control arm consists of participants who do not have access to the AI-based platform. They will perform the same tasks or assessments as those in the experimental arm but without the assistance of AI. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
Accuracy represents how closely a result aligns with the true value or standard. Accuracy of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. the reference for the comparison is the consensus of three experts in dentistry.
Measured: through data collection, an average of 6 months
Sensitivity
This measure quantifies the proportion of true positive results (correctly identified cases) out of all positive cases. High sensitivity indicates that one is good at detecting the condition. Sensitivity of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. The comparison is made against the consensus judgment of three experts in dentistry.
Measured: through data collection, an average of 6 months
Specificity
Specificity measures the proportion of true negative results out of all negative cases. Specificity of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. The comparison is made against the consensus judgment of three experts in dentistry.
Measured: through data collection, an average of 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jun 2024
- Study start30 Jul 2024 (estimated)
- Primary completion13 Nov 2024 (estimated)
- Study completion13 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Endodontically Treated TeethStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Copenhagen
- Sponsor type
- University, hospital or other
- Collaborators
- Queen Mary University of London
- Responsible party
- Principal investigator, Lars Bjørndal, University of Copenhagen
- Investigators
- Lars Bjørndal, Prof., Principal investigator, University of Copenhagen Department of Odontology Cariology and Endodontics
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06450938
- Sponsor's protocol ID
- 38LZDNHFH5JN
- Status last verified
- Jun 2024
- First submitted
- 25 May 2024
- Last updated
- 25 Jun 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Implementing a Corrective Annotation No Code Artificial Intelligence-based Software to Detect Several Radiographic Features Associated With Unsatisfactory Endodontic Treatment: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT06450938
- Acronym / sponsor ID
- 38LZDNHFH5JN
- Conditions
- Endodontically Treated Teeth, Endodontic Underfill, Endodontic Overfill, Apical Periodontitis
- Keywords
- Artificial Intelligence, Endodontic treatment
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- University of CopenhagenUniversity, hospital or other
- Collaborators
- Queen Mary University of London
- Study start
- 30 Jul 2024 (estimated)
- Primary completion
- 13 Nov 2024 (estimated)
- Study completion
- 13 Dec 2024 (estimated)
- First posted
- 10 Jun 2024 (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.
Developing neural network-based models for image analysis can be time-consuming, requiring dataset design and model training. No-code AI platforms allow users to annotate object features without coding. Corrective annotation, a "human-in-the-loop" approach, refines AI segmentations iteratively. Dentistry has seen success with no-code AI for segmenting dental restorations. This study aims to assess radiographic features related to root canal treatment quality using a "human-in-the-loop" approach.
Detailed description
The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potential in finding radiographic features and treatment planning in the field of cariology and endodontics. A growing body of literature suggests that DL models might assist dental practitioners in detecting radiographic features such as carious lesions, and periapical lesions, as well as predicting the risk of pulp exposure when doing caries excavation therapy. Although, the current literature lacks sufficient research on the interaction of participants and AI in an AI-based platform for detecting features associated with technical quality of endodontic treatment. This prospective randomized controlled trial aims to assess the performance of students when using an AI-based platform for detecting features associated with technical quality of endodontic treatment and predicting the long term prognosis of the treatment. The hypothesis is that participants' performance in the group with access to AI responses is similar to the control group without access to AI responses.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| participants using guidance from artificial Intelligence | Experimental | Device: AI guidance for finding radiographic featuresthe experimental arm refers to the group of participants who have access to the AI-based platform for detecting features associated with the technical quality of endodontic treatment. These participants will utilize the AI assistance during the study. |
| Control arm without any guidance from artificial Intelligence | No intervention | –the control arm consists of participants who do not have access to the AI-based platform. They will perform the same tasks or assessments as those in the experimental arm but without the assistance of AI. |
Who may qualify
- Ages
- 20 to 40 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (1 listed)
- 1.Being a last year dental student at the university of Copenhagen
Key exclusion criteria (2 listed)
- Having any previous AI-related experiences
- Not accepting to sign the 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.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI guidance for finding radiographic featuresA secured website was made for the trial in which each student could log in using the assigned number. All the image datasets were uploaded to this website. The students will be randomly assigned to the experiment and control group. Both students were asked to segment the features associated with the quality of root canal treatment and predict the prognosis of treatment while the experiment group had access to AI guidance and the control group didn't. | Device | participants using guidance from artificial Intelligence |
Primary outcome
Accuracy
Time frame: through data collection, an average of 6 months
Accuracy represents how closely a result aligns with the true value or standard. Accuracy of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. the reference for the comparison is the consensus of three experts in dentistry.
Sensitivity
Time frame: through data collection, an average of 6 months
This measure quantifies the proportion of true positive results (correctly identified cases) out of all positive cases. High sensitivity indicates that one is good at detecting the condition. Sensitivity of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. The comparison is made against the consensus judgment of three experts in dentistry.
Specificity
Time frame: through data collection, an average of 6 months
Specificity measures the proportion of true negative results out of all negative cases. Specificity of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. The comparison is made against the consensus judgment of three experts in dentistry.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jun 2024.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Lars Bjørndal, Prof. principal investigator
University of Copenhagen Department of Odontology Cariology and Endodontics
Questions about this trial
What is this clinical trial studying?
A study testing AI guidance for finding radiographic features in people with Endodontically Treated Teeth and Endodontic Underfill, run by University of Copenhagen.
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 2 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: status not confirmed, as of the sponsor's last update on 25 Jun 2024. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Shaqayeq Ramezanzade, Phd (55278370, shaqayeq.ramezanzade@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: through data collection, an average of 6 months). The study lists 3 primary outcomes in all. 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 25 Jun 2024.