Gait-based diagnostic network for localization and pathological characterization of spine and pelvis diseases

Spine and pelvis diseases impair motor control through pain and neurological dysfunction, resulting in characteristic gait abnormalities. However, existing clinical screening and initial triage rely on static imaging, which overlooks gait patterns and limits efficiency for the primary assessment of these diseases. To enhance efficiency and enable clinically aligned diagnostics, we utilize gait videos and propose a hierarchical diagnostic model for spine and pelvis diseases. Based on gait videos, our method progresses from disease-region localization to pathological characterization, explicitly mimicking the clinical diagnostic process. To capture dynamic gait features, we leverage a Spatiotemporal Transformer that jointly models structural dependencies among body regions and motion rhythms. To ensure diagnostic coherence, we design a clinically inspired Tree Hierarchy Classifier that integrates region localization and pathology assessment, while preventing incompatible cross-level predictions. We collected clinical gait videos from 419 patients to train and validate our proposed model. At the first diagnostic level, the model distinguishes cervical/thoracic from lumbar/pelvis diseases, achieving an accuracy of 76% and an AUC of 85%. The second diagnostic level performs pathological characterization, further classifying cases into tumor and non-tumor subtypes across these regions. We further provide clinical interpretability via spatiotemporal attention visualizations, revealing that the model captures gait patterns consistent with established pathophysiological mechanisms. By aligning gait patterns with the dynamic functional impact of disease, we demonstrate the potential of gait video analysis as an efficient approach for the primary screening of spine and pelvis diseases.