Nearly 3 in 10 adults in the United States have experienced lower back pain in any three months, making it the most common musculoskeletal pain. Back pain is one of the leading causes of disability worldwide, often resulting in chronic discomfort, missed work, and invasive procedures. Current lumbar spine modeling shows promise for treatment planning, but its slow, manual process and reliance on specialized expertise limit scalability and personalization. Now, a new artificial intelligence (AI) spine model could transform lower back pain treatment.

Researchers at Florida Atlantic University (Boca Raton, FL, USA), in collaboration with the Marcus Neuroscience Institute at Baptist Health (Boca Raton, FL, USA), have created a fully automated finite element analysis pipeline for lumbar spine modeling. Their innovation integrates AI tools such as nnUNet and MONAI with biomechanical simulators like GIBBON and FEBio. The system automatically converts standard medical images, including CT and MRI scans, into patient-specific models, mapping bones, cartilage, and ligaments before simulating spine movement and stress distribution.

Results published in World Neurosurgery showed that this automated pipeline reduced model preparation time by 97.9%, cutting it from more than 24 hours to just 30 minutes and 49 seconds. Tests confirmed that the virtual spine reacted like a real one, demonstrating realistic disc movement, ligament tension, and pressure during bending and stretching. By removing manual steps, the system delivered consistent, high-quality results without compromising biomechanical accuracy.

This breakthrough enables rapid, patient-specific simulations that can be used for preoperative planning, spinal implant optimization, and early detection of degenerative conditions. It improves speed and consistency, allowing clinicians to make more informed treatment decisions. Going forward, the researchers expect their technology to play a growing role in redefining spine care by uniting engineering with medicine to address complex musculoskeletal challenges.

“What sets our approach apart is its ability to automatically convert standard medical images like CT or MRI scans into highly accurate, patient-specific spine models,” said Maohua Lin, Ph.D., corresponding author and research assistant professor at the FAU Department of Biomedical Engineering. “Traditional manual methods require complex geometry processing, meshing, and finite element simulation setup, making them not only time-intensive but also highly dependent on the operator’s expertise. Our automated pipeline significantly reduces the time required, cutting what once took several hours or even days down to just minutes.”

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