AI Enabled Automated Digital Image Analysis for Peri-Implant Health Monitoring: A Preliminary Diagnostic Study
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Published on: June 10, 2025
Xinyu Wu1, Xindi Wei1, Xiaolei Lv1
1Department of Oral and Maxillofacial Implantology, Shanghai Ninth People Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center of Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China.
Objectives:
To develop and validate an AI-driven Digital Erythema Score (DES) for objective, non-invasive assessment of peri-implant mucosal erythema using intraoral scan (IOS) data.
Materials And Methods:
An internal dataset (200 patients, 265 implants) was utilized, with the reference standard established via consensus by two independent experts. We developed a U-Net model to automatically segment the peri-implant mucosal margins to calculate the DES and diagnose erythema (modified Gingival Index mGI 0, ≥ 1). We used the YOLO model for object detection to localize erythema regions. The system's diagnostic performance was validated on an independent external dataset (23 patients, 30 implants), supplemented by a multi-observer study to evaluate the AI's assistance across clinicians with varying experience levels.
Results:
The U-Net model achieved precise ROI segmentation (Dice similarity coefficient 0.69 ± 0.11) and reliable colorimetric calculation (ICC > 0.95). At the optimal threshold, the DES accurately identified mucosal erythema (AUC, internal 0.9228 [95% CI: 0.8533 to 0.9760], external 0.9018 [0.8135 to 0.9773]). DES and mGI showed significant correlations with bleeding on probing (B = 7.38, OR = 5.32, both p < 0.001). The YOLO model localized inflammatory regions (mAP internal 0.8968, external 0.6820). In the external validation, the DES-driven diagnostic framework enhanced the diagnostic performance of observers with different levels of experience and increased the inter-observer consistency.
Conclusions:
The DES provides an objective metric that converts chromatic signals into a diagnostic score and is significantly correlated with clinical inflammation. This AI-driven framework reduces clinical subjectivity and establishes a non-invasive paradigm for teledentistry and high-frequency, home-based monitoring of peri-implant health in the future after validation with patient-accessible imaging devices.
Trial Registration:
This study has been prospectively registered (NCT07349095).
