Peri-implant MucositisNCT07349095
AI-Assisted Colorimetric Diagnosis of Peri-Implant Mucosal Erythema
A study testing AIa assisted diagnosis in people with Peri-implant Mucositis, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Peri-implant Mucositis
- What is being tested?
- AIa assisted diagnosis
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2025to 27 Feb 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Department of Oral Maxillofacial Implantology Shanghai Ninth People's Hospital
Shanghai
Xinyu Wu, PhD, 021 5331 4050, wuxinyu_dentist@hotmail.com
About this study
Official title: 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
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
- Conditions
- Peri-implant Mucositis
- Being tested
- AIa assisted diagnosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIa assisted diagnosisDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| AI-Assisted Diagnostic Evaluation for Peri-Implant Mucosal Erythema | Experimental | 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. Receives: Diagnostic Test: AIa assisted diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of the AI system for detecting peri-implant mucosal erythema, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC).
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.
Measured: At the completion of image analysis for the external validation set, approximately 3 months after study start
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jan 2026
- Study start1 Sep 2025
- Primary completion30 Jan 2026 (estimated)
- Study completion27 Feb 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Peri-implant MucositisStudies of the same condition, open and under way.
- More trials run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Junyu Shi, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07349095
- Sponsor's protocol ID
- SH9H-2025-196-imp
- Status last verified
- Jan 2026
- First submitted
- 9 Jan 2026
- Last updated
- 16 Jan 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Wu X, Wei X, Lv X, Fu X, He Z, Yin W, Lai H, Shi J. AI Enabled Automated Digital Image Analysis for Peri-Implant Health Monitoring: A Preliminary Diagnostic Study. Clin Oral Implants Res. 2026 Nov 1. doi: 10.1111/clr.70200. Online ahead of print.
PubMed 42968416
Study at a glance
From the registry record, last updated by the sponsor on 16 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07349095
- Acronym / sponsor ID
- SH9H-2025-196-imp
- Conditions
- Peri-implant Mucositis
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong UniversityUniversity, hospital or other
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 30 Jan 2026 (estimated)
- Study completion
- 27 Feb 2026 (estimated)
- First posted
- 16 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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 design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Diagnostic Evaluation for Peri-Implant Mucosal Erythema | Experimental | Diagnostic Test: AIa assisted diagnosisParticipants 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AIa assisted diagnosisParticipants 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 | AI-Assisted Diagnostic Evaluation for Peri-Implant Mucosal Erythema |
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Jan 2026.
- Derived from this study: PubMed 42968416Wu X, Wei X, Lv X, Fu X, He Z, Yin W, Lai H, Shi J. AI Enabled Automated Digital Image Analysis for Peri-Implant Health Monitoring: A Preliminary Diagnostic Study. Clin Oral Implants Res. 2026 Nov 1. doi: 10.1111/clr.70200. Online ahead of print.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AIa assisted diagnosis in people with Peri-implant Mucositis, run by Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 16 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Junyu Shi, Professor (021 5331 4050, sakyamuni_jin@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07349095, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Jan 2026.