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
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 20
- 试验地点
- 2
- 主要终点
- Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- Double (Participant, Outcomes Assessor)
入排标准
- 年龄范围
- 20 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- 未提供
结局指标
主要结局
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity
时间窗: 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
时间窗: 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
时间窗: 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).
次要结局
未报告次要终点