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

NCT06450938 | Not Yet Recruiting

• 1 other identifier: 38LZDNHFH5JN
• Study Type: interventional
• Target: 80
• Locations: 0 countries
• Sites: N/A

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.

Conditions

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

Keywords

Artificial Intelligence; Endodontic treatment

Outcome Measures

Primary Outcomes (3)

• 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

• 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

• 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

Study Arms (2)

participants using guidance from artificial Intelligence

EXPERIMENTAL

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

Control arm without any guidance from artificial Intelligence

NO INTERVENTION

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.

Interventions

AI guidance for finding radiographic features DEVICE

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.

participants using guidance from artificial Intelligence

Eligibility Criteria

• Age: 20 Years - 40 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• Being a last year dental student at the university of Copenhagen

You may not qualify if:

• Having any previous AI-related experiences
• Not accepting to sign the informed consent

Sponsors & Collaborators

• University of Copenhagen: lead
• Queen Mary University of London: collaborator

MeSH Terms

Conditions

Tooth, Nonvital; Periapical Periodontitis

Condition Hierarchy (Ancestors)

Dental Pulp Diseases; Tooth Diseases; Stomatognathic Diseases; Periapical Diseases; Jaw Diseases; Periodontal Diseases; Mouth Diseases; Periodontitis

Study Officials

• Lars Bjørndal, Prof.

  University of Copenhagen Department of Odontology Cariology and Endodontics

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Shaqayeq Ramezanzade, Phd

CONTACT

55278370; shaqayeq.ramezanzade@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: May 25, 2024
• First Posted: June 10, 2024
• Study Start: July 30, 2024
• Primary Completion: November 13, 2024
• Study Completion: December 13, 2024
• Last Updated: June 25, 2024

Record last verified: 2024-06

Data Sharing

• IPD Sharing: Will not share