Ignite Creation Date:
2024-06-16 @ 11:52 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06450938
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-25
First Post:
2024-05-25
Brief Title:
No Code Artificial Intelligence to Detect Radiographic Features Associated With Unsatisfactory Endodontic Treatment
Sponsor:
University of Copenhagen
Organization:
University of Copenhagen
Study Overview
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
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None