The Impact of Training Dental Students for Using a Novel Artificial Intelligence-based Platform for Pulp Exposure Prediction Before Deep Caries Excavation: A Randomized Controlled Trial
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- University of Copenhagen
- Enrollment
- 20
- Locations
- 2
- Primary Endpoint
- Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity
Study Overview
Brief Summary
The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potentials 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 radiographical features such as carious lesions, periapical lesions, as well as predicting the risk of pulp exposure when doing caries excavation therapy. Although, current literature lacks sufficient research on the effect of sufficient training of dental practitioners for using AI-based platforms. This prospective randomized controlled trial aims to assess the performance of students when using an AI-based platform for pulp exposure prediction with and without sufficient preprocedural training. The hypothesis is that participants performance at group with sufficient training is similar to the group without sufficient training.
Study Design
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel
- Primary Purpose
- Other
- Masking
- Double (Participant, Outcomes Assessor)
Eligibility Criteria
- Ages
- 20 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •perhaps 4th year and 5th year dental students at the university of Copenhagen who are willing to participate voluntarily and have signed the consent letter.
- •Limited or no previous knowledge and experience about AI
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity
Time Frame: 30 days
The sensitivity of students at both group (with and without training session) will be measured and compared together. It will be based on the proportion of actual pulp exposure cases that got predicted as pulp exposure (true positive).
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their accuracy
Time Frame: 30 days
The accuracy of students at both group (with and without training session) will be measured and compared together. The accuracy measurement for each student will be calculated by the number of correct predictions of pulp exposure occurrence divided by the total predictions.
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their specificity
Time Frame: 30 days
The specificity of students at both group (with and without training session) will be measured and compared together. It will be based on the proportion of actual 'no pulp exposure' cases correctly predicted as cases without pulp exposure (true negative).
Secondary Outcomes
No secondary outcomes reported