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No Code Artificial Intelligence to Detect Radiographic Features Associated With Unsatisfactory Endodontic Treatment
Sponsor: University of Copenhagen
Summary
Developing neural network-based models for image analysis can be time-consuming, requiring dataset design and model training. No-code AI platforms allow users to annotate object features without coding. Corrective annotation, a "human-in-the-loop" approach, refines AI segmentations iteratively. Dentistry has seen success with no-code AI for segmenting dental restorations. This study aims to assess radiographic features related to root canal treatment quality using a "human-in-the-loop" approach.
Official title: Implementing a Corrective Annotation No Code Artificial Intelligence-based Software to Detect Several Radiographic Features Associated With Unsatisfactory Endodontic Treatment: A Randomized Controlled Trial
Key Details
Gender
All
Age Range
20 Years - 40 Years
Study Type
INTERVENTIONAL
Enrollment
80
Start Date
2024-07-30
Completion Date
2024-12-13
Last Updated
2024-06-25
Healthy Volunteers
Yes
Interventions
AI guidance for finding radiographic features
A secured website was made for the trial in which each student could log in using the assigned number. All the image datasets were uploaded to this website. The students will be randomly assigned to the experiment and control group. Both students were asked to segment the features associated with the quality of root canal treatment and predict the prognosis of treatment while the experiment group had access to AI guidance and the control group didn't.