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Scoliosis is the three-dimensional (3D) deformity of the spine and trunk. The majority or 80% of the scoliosis cases are idiopathic which means that the cause is unknown. The progression of scoliosis is much more frequently seen in females and severely scoliotic patients face a higher risk of different health problems in their adult life Bracing is the most commonly used non-invasive treatment for patients with smaller spinal curves which are usually between 21 and 40 degrees at Risser stage 0, 1 or 2. However, scoliosis braces are traditionally constructed based on the experience of orthotists. Since 3D spinal deformities are complex, the design and evaluation of braces are challenging with no consensus on an optimal brace design. AI and ML methods therefore constitute a new approach to address the difficulties in designing braces.
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The designs of flexible brace include: an artificial hinge bone is used to stabilize the corrective components. Corrective straps are attached to the artificial hinge to exert corrective forces onto the scoliotic spine. Semi-rigid silicon pads with high conformability to the body are used to enhance the corrective forces.
This study is a multi-disciplinary collaborative project with experts in computer science, orthopaedics, materials science, garment technology, engineering and biomechanics. A biomechanical computational model will be developed to recommend modifications to the flexible brace design. The effectiveness of the flexible braces will be evaluated through low-dose X-ray radiography at pre-, post-expert and post-ML designed treatment. A polynomial scoring system will be created to assess the therapeutic similarity of the ML-designed flexible brace to the expert-designed flexible brace based on an estimate of the corrective forces applied through the braces. Reinforcement learning with scores based on estimated pressure force will guide AI training towards superior therapeutic designs.
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100 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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