Interdependence between surface topography and filter size in roughness analysis of dental implant materials

Surface topography significantly influences the success of implants by modulating osseointegration. This study investigates how the choice of Gaussian filter cutoff wavelength (λc) impacts the calculation of surface roughness parameters on titanium implants with different micro- and submicro-scale surface modifications. Titanium plates with four surface modifications (machined (M), acid-etched (A), sandblasted (SL), sandblasted and acid-etched (SLA)) were analyzed using confocal microscopy. Surface data were processed in accordance with ISO 25178, applying Gaussian filters with various λc values (0.008–0.8 mm). Roughness parameters across height, hybrid, spatial, and volume categories were extracted. Statistical evaluations included ANOVA, Wilcoxon tests, and correlation analyses. Most roughness parameters showed significant variation with changing filter sizes, particularly between 0.008 mm and 0.08 mm. Sa, Sq, and void volume were strongly affected, while Sdr, Sdq, and Str remained stable. Highly structured, multiscale surfaces (SLA) exhibited greater parameter sensitivity to filtering, while smoother surfaces (machined) were more robust. Ssk was especially influenced by filter size and surface texture, highlighting its potential to distinguish relevant topographies. The cutoff wavelength λc critically affects roughness results, evaluation and interpretation. Surface-specific filter selection and transparent documentation are essential for reliable characterization and valid biological correlations in implant research. • Filter size selection significantly affects roughness parameter results. • Intrinsic surface topography influences filter-induced deviations to varying degrees. • Interpretation of surface topography based on roughness parameters directly depends on selected filter size. • Comprehensive documentation of filtering procedures is essential for reproducibility.