NCT06450938

No Code Artificial Intelligence to Detect Radiographic Features Associated With Unsatisfactory Endodontic Treatment

Implementing a Corrective Annotation No Code Artificial Intelligence-based Software to Detect Several Radiographic Features Associated With Unsatisfactory Endodontic Treatment: A Randomized Controlled Trial

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 80 (estimated)
• Sponsor: University of Copenhagen · Academic / Other
• Sex: All
• Age: 20 Years – 40 Years
• Healthy volunteers: Accepted

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.

Detailed description

The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potential 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 radiographic features such as carious lesions, and periapical lesions, as well as predicting the risk of pulp exposure when doing caries excavation therapy. Although, the current literature lacks sufficient research on the interaction of participants and AI in an AI-based platform for detecting features associated with technical quality of endodontic treatment. This prospective randomized controlled trial aims to assess the performance of students when using an AI-based platform for detecting features associated with technical quality of endodontic treatment and predicting the long term prognosis of the treatment. The hypothesis is that participants' performance in the group with access to AI responses is similar to the control group without access to AI responses.

Conditions

• Endodontically Treated Teeth
• Endodontic Underfill
• Endodontic Overfill
• Apical Periodontitis

Interventions

Type	Name	Description
DEVICE	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.

Timeline

• Start date: 2024-07-30
• Primary completion: 2024-11-13
• Completion: 2024-12-13
• First posted: 2024-06-10
• Last updated: 2024-06-25