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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06450938
• Other study ID #: 38LZDNHFH5JN
• Status: Not yet recruiting
• Phase: N/A
• Start date: May 30, 2024
• Est. completion date: December 13, 2024

Study information

• Verified date: June 2024
• Source: University of Copenhagen
• Contact: Shaqayeq Ramezanzade, Phd
• Phone: 55278370
• Email: shaqayeq.ramezanzade[@]gmail.com
• Is FDA regulated: No
• Study type: Interventional

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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 80
• Est. completion date: December 13, 2024
• Est. primary completion date: November 13, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 20 Years to 40 Years

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

1. Having any previous AI-related experiences

2. Not accepting to sign the informed consent

Related Conditions & MeSH terms

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

Intervention

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.

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
University of Copenhagen	Queen Mary University of London

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accuracy	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.	through data collection, an average of 6 months
Primary	Sensitivity	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.	through data collection, an average of 6 months
Primary	Specificity	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.	through data collection, an average of 6 months