AI-Assisted Colorimetric Diagnosis of Peri-Implant Mucosal Erythema

January 9, 2026 updated by: Junyu Shi, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University

A Diagnostic Study to Develop and Validate an Artificial Intelligence-Based Colorimetric System for the Objective Diagnosis of Peri-Implant Mucosal Erythema and to Evaluate Its Impact on Clinician Performance

  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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

200

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
      • Shanghai, China
        • Recruiting
        • Department of Oral Maxillofacial Implantology Shanghai Ninth People's Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Consecutive patients aged 18 and above, with single or splinted implant-supported restorations visiting the Department of Oral and Maxillofacial Implantology Shanghai Ninth People's Hospital for regular implant maintenance will be included. Participants were excluded if i) pregnancy or intention to become pregnant; ii) with any systemic diseases/conditions that are contraindications to dental implant treatment; and iii) inability or unwillingness to give written informed consent.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-Assisted Diagnostic Evaluation for Peri-Implant Mucosal Erythema
Participants in this single-arm study undergo evaluation using the investigational AI-based colorimetric system. The study involves two distinct participant roles: 1) Patient Participants who have previously received intraoral scans contribute their de-identified digital dental images (3D surface files and 2D screenshots) for AI model development and validation. 2) Clinician Participants (including experts, general dentists, and students) take part in a prospective observer study. In a controlled, crossover manner, they diagnose a standardized set of peri-implant mucosal images first without any aid, and then with the assistance of the AI system, which provides an objective color index value and visual bounding boxes around suspected erythematous regions. The primary aim for this arm is to assess the diagnostic accuracy, reliability, and clinical utility of the AI system across both technical (vs. expert reference) and human (clinician performance enhancement) endpoints.
Diagnostic test: AIa assisted diagnosis
Participants in this single-arm study undergo evaluation using the investigational AI-based colorimetric system. The study involves two distinct participant roles: 1) Patient Participants who have previously received intraoral scans contribute their de-identified digital dental images (3D surface files and 2D screenshots) for AI model development and validation. 2) Clinician Participants (including experts, general dentists, and students) take part in a prospective observer study. In a controlled, crossover manner, they diagnose a standardized set of peri-implant mucosal images first without any aid, and then with the assistance of the AI system, which provides an objective color index value and visual bounding boxes around suspected erythematous regions. The primary aim for this arm is to assess the diagnostic accuracy, reliability, and clinical utility of the AI system across both technical (vs. expert reference) and human (clinician performance enhancement) endpoints.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic accuracy of the AI system for detecting peri-implant mucosal erythema, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC).
Time Frame: At the completion of image analysis for the external validation set, approximately 3 months after study start
The primary outcome is the diagnostic accuracy of the AI-based colorimetric system in classifying an image as showing erythema or not. Accuracy is quantified by the Area Under the Receiver Operating Characteristic Curve (AUC), with expert visual diagnosis serving as the reference standard. The AUC, along with its 95% confidence interval, will be calculated separately for the internal development set and the independent external validation set to assess model performance and generalizability.
At the completion of image analysis for the external validation set, approximately 3 months after study start

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 1, 2025

Primary Completion (Estimated)

January 30, 2026

Study Completion (Estimated)

February 27, 2026

Study Registration Dates

First Submitted

January 9, 2026

First Submitted That Met QC Criteria

January 9, 2026

First Posted (Estimated)

January 16, 2026

Study Record Updates

Last Update Posted (Estimated)

January 16, 2026

Last Update Submitted That Met QC Criteria

January 9, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • SH9H-2025-196-imp

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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