Epidemiology aims to provide insight into disease causations. Hence, subject groups (cohorts) are analyzed to correlate the subjects' varying lifestyles, their medical properties and diseases. Recently, these cohort studies comprise medical image data. We assess potential relations between image-derived variables of the lumbar spine with lower back pain in a cross-sectional study. Therefore, an Interactive Visual Analysis (IVA) framework was created and tested with 2,540 segmented lumbar spine

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... ata sets. The segmentation results are evaluated and quantified by employing shape-describing variables, such as spine canal curvature and torsion. We analyze mutual dependencies among shape-describing variables and non-image variables, e.g., pain indicators. Therefore, we automatically train a decision tree classifier for each non-image variable. We provide an IVA technique to compare classifiers with a decision tree quality plot. As a first result, we conclude that image-based variables are only sufficient to describe lifestyle factors within the data. A correlation between lumbar spine shape and lower back pain could not be found with the automatically trained classifiers. However, the presented approach is a valuable extension for the IVA of epidemiological data. Hence, relations between non-image variables were successfully detected and described.