Jonathan von See

Background: Cone-beam computed tomography (CBCT) is increasingly applied for the assessment of periodontal bone levels. However, its measurement reliability and consistency depend strongly on image quality parameters such as voxel size, noise, and reconstruction sharpness. With the growing use of CBCT datasets for artificial intelligence (AI)-based diagnostics, it is essential to understand how image degradation conditions affect examiner-derived measurement outcomes and the reliability of reference data used for AI training. Methods: An anonymized CBCT dataset containing one periodontally healthy tooth (31) and one tooth with pronounced periodontal bone loss (41) was analyzed. The original DICOM data were systematically degraded using controlled voxel enlargement (double and triple voxel size) and simulated image blur (Gaussian and median filtering). Six dentists (n = 6) independently performed standardized linear bone-level measurements, with three repeated measurements per tooth and image condition. Data were analyzed using the Shapiro–Wilk test for normality assessment, the Kruskal–Wallis H test for group comparisons, Bonferroni-adjusted Mann–Whitney U tests for post hoc pairwise comparisons, and intraclass correlation coefficients (ICC (2,1)) for inter-examiner reliability assessment. Results: A total of 180 measurements were evaluated. Image degradation conditions were associated with statistically significant differences in bone-level measurements for both teeth (tooth 31: p = 0.017; tooth 41: p = 0.0049). Significant pairwise differences were primarily observed between the original dataset and specific degraded conditions involving blur and reduced spatial resolution, while several comparisons remained non-significant. Inter-examiner reliability varied across image groups and decreased notably with pronounced voxel enlargement, particularly in the periodontally compromised tooth. Conclusions: Controlled image degradation conditions of CBCT image quality significantly affect measurement outcomes and inter-examiner reproducibility of periodontal bone measurements. These findings demonstrate that image quality is a critical determinant of measurement reliability and examiner-dependent interpretation. From both a clinical and AI-development perspective, maintaining adequate CBCT resolution may contribute to more consistent measurement behavior and more reliable training datasets.