Leveraging Rich Mechanical Features and Long-Range Physical Constraints for Lumbar Spine Stress Analysis.

OBJECTIVE: The biomechanical properties of the lumbar spine is crucial for assisting the diagnosis, treatment, and prevention of spinal diseases. Traditional biomechanical analysis methods, especially the finite element analysis, require extensive computational resources, precise material property definitions, and complex meshing processes to accurately model the biomechanical behavior of the lumbar spine. While deep learning is introduced to enhance efficiency and accuracy, challenges like data dependency and lack of physical consistency remain. METHODS: We propose a novel framework that consists of a 3D generative adversarial network for data augmentation together with a dual-channel vision transformer to extract geometric and physical information. We also introduce a physics-guided mechanism into training phase, ensuring model consistency with mechanical principles. RESULTS: The proposed method achieved an Intersection over Union of 0.8332 and a Mean Squared Error of 0.0002. The five vertebrae of the lumbar spine are processed in 87 milliseconds, which is approximately 3000 times faster than traditional finite element methods. CONCLUSION: Our framework demonstrates high accuracy and substantial computational efficiency, offering a reliable alternative to conventional biomechanical modeling. SIGNIFICANCE: This enables real-time lumbar spine analysis for diagnosis, surgical planning, and personalized treatment.