Apical Periodontitis, Endodontically Treated Teeth, Endodontic Overfill, Endodontic Underfill
Conditions
Keywords
Artificial Intelligence, Endodontic treatment
Brief 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. 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.
Interventions
DEVICEAI guidance for finding radiographic features
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.
Sponsors
Queen Mary University of London
University of Copenhagen
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
20 Years to 40 Years
Healthy volunteers
Yes
Inclusion criteria
1.Being a last year dental student at the university of Copenhagen
Exclusion criteria
1. Having any previous AI-related experiences 2. Not accepting to sign the informed consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy | 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 | 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 | 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. |
Contacts
Primary ContactShaqayeq Ramezanzade, Phd
Outcome results
None listed