Spinal Disease Classification Using Deep Learning on Dual-View Videos

Adult spinal deformity (ASD) is characterized by spinal deformities that cause symptoms such as balance disorders. Although gait observation is used for the diagnosis of ASD, no quantitative evaluation protocol has been established. In this study, we propose a novel approach for distinguishing ASD from non-ASD using a neural network with gait videos captured from two perspectives, enabling the detection of unique gait fluctuations specific to each patient. By integrating spatiotemporal gait dynamics, the proposed method enhances quantitative diagnostics and provides a more comprehensive gait assessment. The experimental results indicate that, relative to a model employing a singular perspective, there was a modest enhancement in classification accuracy accompanied by a notable improvement in the F1 score. Specifically, the F1 score increased by 10% over the single-view model, achieving an F1 score of 71.86%. These results confirm the efficacy of integrating two-perspective videos for gait analysis in ASD and suggest that further investigation into fusion techniques may be beneficial. Codes and models are available at https://github.com/TsuguTsukumo/2stream_3D_CNN_Walk_Pytorch.git.