Impact of Training Dental Students for an AI-Based Platform
- Conditions
- Artificial IntelligenceEducation
- Interventions
- Behavioral: receiving a one hour theoretical and hands on training session before using an AI-based platform
- Registration Number
- NCT05912361
- Lead Sponsor
- University of Copenhagen
- 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 20
- 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
- None
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Students using AI-platform for assessing the risk of pulp exposure receiving a training session receiving a one hour theoretical and hands on training session before using an AI-based platform Students will go through a one-hour hands-on training session before taking the test at the online platform. The session includes a theoretical session related to basic aspects of AI in radiology, CNN (Convolutional Neural Network) applications for cariology and endodontics, as well as basics of excavation therapy and pulp exposure. the theoretical part will be followed by a hands on session on which participants check 11 cases of teeth with deep caries and will find the closest line between caries and pulp. Then, they will receive access to log in to the website on which pretreatment x-rays of cases undergoing caries excavation therapy is uploaded. The performance of students on will be assessed.
- Primary Outcome Measures
Name Time Method Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity 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 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 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 Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
University of Copenhagen Department of Odontology Cariology and Endodontics Section for Clinical Oral Microbiology
🇩🇰Copenhagen, Denmark