Health TrialsbyHealthPanel

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

ConditionsEndodontically Treated TeethEndodontic UnderfillEndodontic OverfillApical Periodontitis
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age20-40
SponsorUniversity of Copenhagen

About this 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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

None

Disqualifiers

Having any previous AI-related experiences

Not accepting to sign the informed consent

Trial design

Design model

Parallel

Treatments tested in this trial

  • 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.

Treatment groups

80 Participants
are divided into 2 treatment groups
Group A: participants using guidance from artificial IntelligenceExperimental treatment 1 intervention
Group B: Control arm without any guidance from artificial IntelligenceNo intervention 0 interventions

Trial outcomes

Primary outcomes

1

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.

Time frame
through data collection, an average of 6 months
2

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.

Time frame
through data collection, an average of 6 months
3

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.

Time frame
through data collection, an average of 6 months

Secondary outcomes

Other outcomes

Locations

This trial has no locations.

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

University of Copenhagen

Lead sponsor

Queen Mary University of London

Collaborator

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: