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

NCT06450938 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 80

Last updated 2024-06-25

No results posted yet for this study

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

Interventions

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.

Sponsors & Collaborators

  • Queen Mary University of London

    collaborator OTHER

  • University of Copenhagen

    lead OTHER

Principal Investigators

  • Lars Bjørndal, Prof. · University of Copenhagen Department of Odontology Cariology and Endodontics

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
DOUBLE
Model
PARALLEL

Eligibility

Min Age
20 Years
Max Age
40 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-07-30
Primary Completion
2024-11-13
Completion
2024-12-13

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06450938 on ClinicalTrials.gov