Ultrasound-based assessment of peri-implant mucosal thickness: an ex vivo comparative study with artificial intelligence-assisted image analysis.
Authors
Affiliations (5)
Affiliations (5)
- Division of Restorative Dentistry, Periodontology and Prosthodontics, Department of Dental Medicine and Oral Health, Medical University Graz, Graz, 8010, Austria.
- Department of Prosthodontics and Biomaterials, Center for Implantology, Medical School RWTH Aachen University, Aachen, 52074, Germany.
- Chair of Medical Engineering, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, 52074, Germany.
- Division of Restorative Dentistry, Periodontology and Prosthodontics, Department of Dental Medicine and Oral Health, Medical University Graz, Graz, 8010, Austria. [email protected].
- Department of Prosthodontics and Biomaterials, Center for Implantology, Medical School RWTH Aachen University, Aachen, 52074, Germany. [email protected].
Abstract
Mucosal thickness (MT) is a key factor influencing peri-implant soft-tissue response and outcomes in dental implantology. This ex vivo study evaluated the agreement of peri-implant MT measurements obtained using ultrasound (US) standardized with a custom probe holder, compared with transgingival probing (TP) and cone-beam computed tomography (CBCT). Porcine hemimandibles (n = 18) underwent guided implant placement. MT was measured at five standardized points using four approaches: (1) US with expert annotation, (2) US with artificial intelligence (AI)-based image segmentation, (3) CBCT, and (4) TP. US images (17 MHz) were independently annotated by two trained specialists; a deep-learning-based method was used to derive automated MT measurements. Method differences were analyzed using a linear mixed-effects model; agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The overall method effect was not significant (p = 0.105). Pairwise comparisons showed no significant difference between expert-annotated US and TP (p = 0.328), whereas CBCT yielded higher MT values than TP (p = 0.035). Agreement was moderate for expert-annotated US versus TP (ICC = 0.58; 95% confidence interval (CI): 0.42-0.70) and for expert-annotated US versus AI-segmented US (ICC = 0.67; 95% CI: 0.53-0.77), but poor for expert-annotated US versus CBCT (ICC = 0.14; 95% CI: -0.05-0.33). Bland-Altman analysis showed mean differences (95% limits of agreement) of 0.08 mm (- 0.96 mm to + 1.13 mm) for expert-annotated US - TP, - 0.01 mm (- 0.68 mm to + 0.67 mm) for expert-annotated US-AI-segmented US, and - 0.30 mm (- 2.29 mm to + 1.68 mm) for expert-annotated US-CBCT. Under controlled ex vivo conditions, expert-annotated US standardized with a custom probe holder showed moderate comparative agreement with TP, while AI-segmented measurements showed moderate agreement with expert annotation. CBCT showed limited agreement with US. This integrated approach represents a proof-of-concept requiring further in vivo validation.