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The Impact of Training Dental Students to Use an Artificial Intelligence-Based Platform for Pulp Exposure Prediction
Shaqayeq Ramezanzade1, Tudor Laurentiu Dascalu2, Azam Bakhshandeh1
1Department of Cariology and Endodontics, Section for Clinical Oral Microbiology, Faculty of Health and Medical Sciences, Department of Odontology, University of Copenhagen, Copenhagen, Denmark.
Aim:
This study evaluated the effect of a short, personalised training session on student performance in using an artificial intelligence (AI)-based platform for pulp exposure prediction before caries excavation and determined the required sample size for a further randomised controlled trial (RCT).
Methodology:
Undergraduate dental students were randomly assigned to the experimental (training) group and the control (no training) group. The training group received a 1-h training session before undertaking the experiment, focusing on the uses, applications, and drawbacks of AI and carious lesion penetration depth. The theoretical presentation was followed by practical exercises and a quiz to check learning progress. Later, participants in both groups completed an experimental task involving 292 cases. They were asked to predict pulp exposure using an AI-based website. Sample size calculations determined the required sample size, with 80% power and an alpha of 5%.
Results:
18 participants were enrolled (9 in each group). The agreement between participants' decisions and AI predictions regarding the presence or absence of pulp exposure (agreeableness with AI) was higher in the training group compared to the control group (0.83 vs. 0.76). The training group had a slightly higher mean F1-score (0.63 vs. 0.62), accuracy (0.69 vs. 0.68), and sensitivity (0.63 vs. 0.59) than the control group. Based on the sample size calculation, at least 31 participants per group are needed for the future RCT.
Conclusions:
The results support further investigation of customised training sessions prior to using an AI-based platform to assess their impact on dental students' agreement with AI predictions.
Trial Registration:
ClinicalTrial.gov identifier: NCT05912361.
