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

Not Applicable

Not yet recruiting

• Conditions: Endodontically Treated Teeth; Endodontic Underfill; Endodontic Overfill; Apical Periodontitis
• Interventions: Device: AI guidance for finding radiographic features

• Registration Number: NCT06450938
• Lead Sponsor: University of Copenhagen

Brief 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.

Study Timeline

• Study First Submitted: 2024-05-25
• Status Verified: 2024-06
• Study First Submitted QC: 2024-06-07
• Study First Posted: 2024-06-10
• Last Update Submitted: 2024-06-24
• Last Update Posted: 2024-06-25
• Study Start: 2024-07-30
• Primary Completion: 2024-11-13
• Study Completion: 2024-12-13

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 80

Inclusion Criteria

1.Being a last year dental student at the university of Copenhagen

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Exclusion Criteria

1. Having any previous AI-related experiences
2. Not accepting to sign the informed consent

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
participants using guidance from artificial Intelligence	AI guidance for finding radiographic features	the experimental arm refers to the group of participants who have access to the AI-based platform for detecting features associated with the technical quality of endodontic treatment. These participants will utilize the AI assistance during the study.

Primary Outcome Measures

Name	Time	Method
Accuracy	through data collection, an average of 6 months	Accuracy represents how closely a result aligns with the true value or standard. Accuracy of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. the reference for the comparison is the consensus of three experts in dentistry.
Sensitivity	through data collection, an average of 6 months	This measure quantifies the proportion of true positive results (correctly identified cases) out of all positive cases. High sensitivity indicates that one is good at detecting the condition. Sensitivity of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. The comparison is made against the consensus judgment of three experts in dentistry.
Specificity	through data collection, an average of 6 months	Specificity measures the proportion of true negative results out of all negative cases. Specificity of participants at experiment and control group in correctly finding the radiographic features and predicting the outcomes is one of our primary outcomes. The comparison is made against the consensus judgment of three experts in dentistry.