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

Apical Periodontitis Clinical Trial

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

Status Not yet recruiting

Clinical Trial 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Apical Periodontitis
• Endodontically Treated Teeth
• Periapical Periodontitis
• Periodontitis
• Tooth, Nonvital

• NCT number: NCT06450938
• Study type: Interventional
• Source: University of Copenhagen
• Contact: Shaqayeq Ramezanzade, Phd
• Phone: 55278370
• Email: shaqayeq.ramezanzade@gmail.com
• Status: Not yet recruiting
• Phase: N/A
• Start date: May 30, 2024
• Completion date: December 13, 2024