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

June 24, 2024 updated by: Lars Bjørndal, University of Copenhagen

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

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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 Type

Interventional

Enrollment (Estimated)

80

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

Yes

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Double

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: participants using guidance from artificial Intelligence
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.
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.
No Intervention: Control arm without any guidance from artificial Intelligence
the control arm consists of participants who do not have access to the AI-based platform. They will perform the same tasks or assessments as those in the experimental arm but without the assistance of AI.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy
Time Frame: 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.
through data collection, an average of 6 months
Sensitivity
Time Frame: 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.
through data collection, an average of 6 months
Specificity
Time Frame: 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.
through data collection, an average of 6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Copenhagen

Collaborators

Queen Mary University of London

Investigators

  • Principal Investigator: Lars Bjørndal, Prof., University of Copenhagen Department of Odontology Cariology and Endodontics

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

July 30, 2024

Primary Completion (Estimated)

November 13, 2024

Study Completion (Estimated)

December 13, 2024

Study Registration Dates

First Submitted

May 25, 2024

First Submitted That Met QC Criteria

June 7, 2024

First Posted (Actual)

June 10, 2024

Study Record Updates

Last Update Posted (Actual)

June 25, 2024

Last Update Submitted That Met QC Criteria

June 24, 2024

Last Verified

June 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 38LZDNHFH5JN

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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