Tokyo, Sept. 1 -- UMIN Clinical Trials Registry (UMIN-CTR) received information related to the study (UMIN000062699) titled 'Development of an AI-Based Feedback System for Tooth Preparation Assessment and Verification of Its Educational Effects on Dental Residents' on Sept. 1.

Study Type: Interventional

Study Design: Basic Design - Parallel Randomization - Randomized Blinding - Open -but assessor(s) are blinded Control - Active

Primary Sponsor: Institute - Okayama University

Condition: Condition - Condition Health Condition(s) or Problem(s) studied:Dental education / Clinical skill training Classification by malignancy - Others Genomic information - NO

Objective: Narrative objectives1 - Tooth preparation for abutments, reducing and shaping teeth to fabricate indirect restorations such as crowns and bridges, is a core clinical skill in prosthodontics that determines the marginal fit and long-term prognosis of prosthetic restorations. However, in the postgraduate clinical training program at our hospital, tooth preparation practice is not part of the formal curriculum, and dental residents currently practice on their own initiative during free time. In this self-directed practice, residents may occasionally receive evaluations when they request them from supervising dentists; however, as supervisors are heavily occupied with clinical and educational duties, such opportunities are limited, and no systematic framework for evaluation and feedback exists. Moreover, even when preparation skills are assessed in undergraduate or postgraduate education, the judgment depends on supervisors' subjective assessment, raising concerns about the objectivity and reproducibility of evaluations (inter- and intra-rater reliability). Consequently, the quality and consistency of feedback to learners are not necessarily assured.

Recently, with the widespread adoption of dental 3D scanners, the morphology of prepared teeth (e.g., taper angle, axial wall height, and continuity of the finish line) can be quantitatively analyzed as digital data. However, no established system automatically scores these data using artificial intelligence (AI) and presents the results as verbal educational feedback.

This study aims to develop an AI-based system that automatically scores and verbalizes the quality of tooth preparation from 3D scan data, and (1) to examine the agreement between AI and supervisor (expert) evaluations, and (2) to investigate the educational effectiveness of immediate AI feedback through a randomized comparison with supervisor feedback. Basic objectives2 - Efficacy

Intervention: Interventions/Control_1 - Intervention group: After tooth preparation, an AI system analyzes STL data obtained via 3D scanning and provides feedback through an AR-based visualization of reduction amount, taper angle, and other scores with evaluative comments.

Control group: After tooth preparation, trainees receive standard verbal feedback from a supervising dentist. Interventions/Control_2 - Control group: After tooth preparation, trainees receive standard verbal feedback from a supervising dentist.

Eligibility: Age-lower limit - Not applicable Age-upper limit - Not applicable Gender - Male and Female Key inclusion criteria - Dental residents in clinical training at Okayama University Hospital who have provided written informed consent to participate in the tooth preparation practice sessions and in this study. Key exclusion criteria - Those who do not provide consent to participate in the study Those otherwise judged ineligible by the principal investigator Target Size - 40

Recruitment Status: Recruitment status - Preinitiation Date of protocol fixation - 2026 Year 07 Month 21 Day Anticipated trial start date - 2027 Year 04 Month 01 Day Last follow-up date - 2030 Year 03 Month 31 Day

To know more, visit https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000071778

Disclaimer: Curated by HT Syndication.