AI-Driven Orthodontic Screening and Assessment
Research type
Research Study
Full title
AI-Driven Orthodontic Screening and Assessment - Intraoral scans Database
IRAS ID
344893
Contact name
Cristele Queli Anselmo da Costa
Contact email
Sponsor organisation
GREENWICH DENTAL PRACTICE
Duration of Study in the UK
10 years, 0 months, 1 days
Research summary
The intraoral scanners database study aims to develop a comprehensive database of intraoral scans and orthodontic assessment records from patients undergoing orthodontic treatment. The primary objective is to use this data to train and validate machine learning algorithms that can assist in identifying the need for orthodontic treatment based on the Index of Orthodontic Treatment Need (IOTN). Additionally, the study will develop algorithms to evaluate treatment outcomes using the Peer Assessment Rating (PAR) and Index of Complexity, Outcome, and Need (ICON) scores.
This research is essential because malocclusion, a common dental condition affecting tooth alignment, significantly impacts oral health and quality of life. By leveraging advanced machine learning techniques, the study aims to improve diagnostic accuracy and treatment planning, ultimately enhancing patient care in orthodontics.
Patients who have received orthodontic treatment at NHS and private dental practices and have consented to participate will have their pseudonymised intraoral scans and assessment records included in the database. The data will be securely stored and accessed only by authorised researchers, ensuring strict adherence to ethical guidelines and data protection regulations.
This study has the potential to streamline the orthodontic diagnostic process, optimise treatment pathways, and reduce waiting times for orthodontic care, thereby benefiting both patients and healthcare providers.REC name
Yorkshire & The Humber - Leeds East Research Ethics Committee
REC reference
24/YH/0221
Date of REC Opinion
16 Dec 2024
REC opinion
Further Information Favourable Opinion