AI-Assisted Colorimetric Diagnosis of Peri-Implant Mucosal Erythema

1. Background and Rationale The visual diagnosis of peri-implant mucosal erythema (redness), a key sign of inflammation, is highly subjective and varies significantly among clinicians, leading to inconsistencies in early detection and monitoring of peri-implant diseases. There is a critical need for an objective, quantitative, and reliable tool to standardize this assessment. Recent advances in artificial intelligence (AI) and colorimetric analysis of digital intraoral scans offer a promising solution to this clinical challenge. 2. Primary Objectives This diagnostic study aims to: Develop and validate a core colorimetric index that objectively quantifies mucosal erythema from digital intraoral scan data. Develop and validate an AI model that automatically calculates this index and provides a binary diagnosis (erythema present/absent) at the image level. Develop and validate a second AI model for precise localization (object detection) of erythematous regions on standard clinical software screenshots. Evaluate the clinical utility of the AI system by assessing its impact on the diagnostic accuracy, consistency, and confidence of clinicians with varying experience levels. 3. Study Design This is a multiphase diagnostic accuracy study conducted at a single academic center. It comprises three sequential phases with independent validation: Phase 1 (Development \& Internal Validation): Analysis of intraoral scans to derive the color index and train the AI models using an internal dataset. Phase 2 (External Technical Validation): Prospective validation of the trained AI models on an independent cohort of patients from a separate branch of the hospital. Phase 3 (Clinical Utility Assessment): A prospective, controlled, observer study where clinicians perform diagnoses with and without AI assistance. 4. Participants and Methods Data Source: Adult patients with dental implants who received intraoral scans using a 3Shape TRIOS 3 scanner.