Ultrasound-based assessment of peri-implant mucosal thickness: an ex vivo comparative study with artificial intelligence-assisted image analysis

Abstract Background 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). Methods 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. Results 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. Conclusions 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.