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

University of Copenhagen

Status: Not yet enrolling

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

Treatments: Device: AI guidance for finding radiographic features

Study type: Interventional

Funder types: Other

Identifiers

NCT06450938

38LZDNHFH5JN

Details and patient eligibility

About

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.

Full 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.

Enrollment: 80 estimated patients

Sex: All

Ages: 20 to 40 years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

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80 participants in 2 patient groups

participants using guidance from artificial Intelligence

Experimental group

Description:

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.

Treatment:

Device: AI guidance for finding radiographic features

Control arm without any guidance from artificial Intelligence

No Intervention group

Description:

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.