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.
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Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their accuracy
Timeframe: 30 days
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity
Timeframe: 30 days
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their specificity
Timeframe: 30 days