An interventional study of AI guidance for finding radiographic features in Endodontically Treated Teeth, Endodontic Underfill and Endodontic Overfill, sponsored by University of Copenhagen. Not yet recruiting. Open to participants aged 20 Years to 40 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-06-25.
Sponsored by University of Copenhagen · Not applicable, Interventional, and Diagnostic
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.
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.
1,635 studies on the registry are indexed under Periodontitis; 327 are open to participants now.
This study's planned enrollment of 80 is above the median of 45 across 1,191 interventional studies indexed under Periodontitis.
Browse Periodontitis studies →University of Copenhagen is the lead sponsor of 450 studies on the registry; 61 are open to participants now.
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1.Being a last year dental student at the university of Copenhagen
Exclusion Criteria:
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.
Device: AI guidance for finding radiographic features
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.
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.
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.
Time frame: 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.
Time frame: 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.
Time frame: through data collection, an average of 6 months
No study locations are listed for this record.
Plan to share: No
No publications or documents are linked to this record.
This study is not yet recruiting, as verified in Jun 2024. You cannot join it, but the record below documents what was studied.
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University of Copenhagen