Spinal disfigurement has been a widespread problem and has continued to grow over the past couple of decades  whether it be due to injury , poor posture or aging . A tool necessary to diagnose spinal deformities are spinal topographies, essentially pictures, which are taken by devices such as the X-ray, CT scanner, or MRI . However, the usage of these devices are expensive and may pose certain risks . This project proposes a method that is safer and more accessible using spinal sketches. With the use of CT scans of human vertebrae , classified into 9 groups: flat, thoracic sway, lumbar sway, 4 variations of scoliosis, and 2 normal variations, will be fed through a Deep Reinforcement Learning Model (DRLM) which sketches the curve of the spine for these 9 groups. This DRLM achieved 98% accuracy which was tested using a Convolutional Neural Network. These sketches are then fed through a machine learning algorithm where it finds 9 separate graphical functions for the 9 groups. A prototypal medical device capable of mapping the vertebrae uses these 9 functions as paths, and creates the morphology of the vertebrae. A pressure sensor on the device records the pressure along the discs of the vertebrae, which with additional variables such as the body composition of the individual, will allow for the recalibration of the device since variability is bound to be present among patients. I believe this research and resulting device will be able to increase availability for assistance with spinal disfigurement and future research.
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