Using Surface Topography to Visualize Spinal Motion During Gait. Motion Analytical Considerations and All Tools for Open Science

Background: Precise segmental spinal analysis during gait has various implications for clinical use and basic research. Here, we report the use of Surface Topography (ST), utilizing DIERS for-metric III 4D™, DICAM v3.7Beta, to analyze three-dimensional spinal segment movements, in combination with foot pressure measuring, to describe individual vertebral bodies’ motion rel-ative to specific phases of gait. All tools and visualizations used in this study have been made freely available in repositories to enable the replication and validation of our findings. Methods: Using a Statistical Analysis System (SAS) script, single files exported by DICAM can be merged to create a complete raw data table. Further SAS script then generates a Standardized Gait Cycle (SGC) for each measurement, including all measured gait cycles for each individual patient, with a spline function to obtain smooth curve progressions. Graph templates from Sta-tistical Package for the Social Sciences software (SPSS v23) then provides us to create detailed visualizations of the SGCs. We tested the further developed system and our data processing tools on measurements obtained from 201 healthy asymptomatic participants (132 females, 69 males). Results: An impressive inter-individual variability as well as intra-individual consistency of spinal motion is shown. Rotation patterns are usually characterized by sinusoidal curve pro-gressions with individually characterizing features. The direction of movement of the pelvis is usually opposite to that of the thoracic spine exhibiting all kinds of phase shifts in their rotation courses. The transformation into a SGC facilitates intra- and inter-individual comparisons. Conclusions: Our concept enables a precise and continuous description of spinal motion in direct relation to gait for qualitative and quantitative analyses. Before standardized data can be used to distinguish between physiologic and pathologic spinal motion, single cases must be scrutinized to identify movement parameters and resulting characteristic patterns. Artificial Intelligence based analysis can facilitate this process.