Uncertainty-Aware Multimodal Gait Representation Learning for Scoliosis Screening.

Early scoliosis screening enables timely intervention and may prevent disease progression. However, gait-based screening remains challenging: abnormalities are often subtle, labeled data are limited, and multimodal analysis must handle cross-modal discrepancy and missing modalities. To address these challenges, we propose Uncertainty-Aware Multimodal Gait Representation Learning (UMGRL), a cross-modal gait representation learning framework that transfers knowledge from large-scale gait data to scoliosis screening. To learn transferable representations robust to missing modalities, we develop a teacher-student paradigm. A shared latent projector and disentanglement module decompose multimodal features into modality-invariant geometry and modality-specific attributes, enabling cross-modal alignment while preserving complementary information. Modality dropout and representation prediction further allow the student to recover task-ready representations of missing modalities from available inputs. For downstream screening, we employ Dynamic Time Warping (DTW) to partition videos into phase-consistent bags for temporal alignment, followed by Perceiver IO-based fusion to capture long-range dependencies across modalities and gait cycles. To enhance sensitivity to borderline cases near the 10° Cobb threshold, we introduce a borderline-focused branch. Finally, we incorporate Evidential Deep Learning (EDL) for uncertainty-aware prediction and reliable triage. Experiments on Scoliosis1K demonstrate superior performance, particularly under incomplete-modality settings and on clinically ambiguous cases. These results suggest that UMGRL provides a promising path toward scalable, radiation-free scoliosis screening.