Clinical trial NCT06450938: No Code Artificial Intelligence to Detect Radiographic Features Associated With Unsatisfactory Endodontic Treatment
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
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Endodontically Treated Teeth
Endodontic Underfill
Endodontic Overfill
Apical Periodontitis
Keywords: Artificial Intelligence
Endodontic treatment
Lead sponsor: University of Copenhagen
Sponsor class: OTHER
Collaborators: name: Queen Mary University of London
First posted: 2024-06-10
Enrollment: 80
Sex: ALL
Minimum age (years): 20
Maximum age (years): 40
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Accuracy
timeFrame: through data collection, an average of 6 months
measure: Sensitivity
timeFrame: through data collection, an average of 6 months
measure: Specificity
timeFrame: through data collection, an average of 6 months
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: University of Copenhagen Department of Odontology Cariology and Endodontics
name: Lars Bjørndal, Prof.
role: PRINCIPAL_INVESTIGATOR
References: