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. Image Data: Two formats are used: 1) Processed 3D surface files (PLY format) for colorimetric analysis, and 2) Standardized 2D screenshots from the 3Shape software for object detection. Reference Standards: Expert consensus on erythema (primary) and Bleeding on Probing (BOP, clinical inflammatory standard). AI Development: Deep learning models (e.g., convolutional neural networks) will be trained for index calculation, image-level diagnosis, and region localization. Observer Study: Participating clinicians (experts, general dentists, and students) will diagnose a set of test images both unaided and with AI assistance (which displays the color index value and/or bounding boxes). 5. Key Outcome Measures Diagnostic Accuracy: Area under the receiver operating characteristic curve (AUC), sensitivity, specificity (with 95% confidence intervals). Technical Performance: Intraclass correlation coefficient (ICC) for automated measurement agreement; Mean Average Precision (mAP) and Dice Similarity Coefficient for object detection. Clinical Impact: Change in diagnostic accuracy (AUC), inter-observer agreement (Kappa), and diagnostic confidence scores when using AI assistance. 6. Significance This study seeks to translate a subjective clinical sign into an objective, AI-powered diagnostic biomarker. If successful, the proposed system could become a valuable decision-support tool in daily practice and clinical research, promoting earlier, more consistent, and standardized monitoring of peri-implant tissue health, ultimately improving patient care.
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Diagnostic accuracy of the AI system for detecting peri-implant mucosal erythema, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC).
Timeframe: At the completion of image analysis for the external validation set, approximately 3 months after study start