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临床试验/NCT05912361
NCT05912361已完成不适用

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

University of Copenhagen2 个研究点 分布在 1 个国家目标入组 20 人开始时间: 2023年8月20日最近更新:
适应症

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

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

研究者

申办方类型
Other
责任方
Sponsor

研究点 (2)

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