Intelligent Evaluation of Global Spinal Alignment by a Decentralized Convolutional Neural Network.

Degenerative changes of the spine can cause spinal misalignment, with part of the spine arching beyond normal limits or moving in an incorrect direction, potentially resulting in back pain and significantly limiting a person's mobility. The most important parameters related to spinal misalignment include pelvic incidence, pelvic tilt, lumbar lordosis, thoracic kyphosis, and cervical lordosis. As a general rule, alignment of the spine for diagnosis and surgical treatment is estimated based on geometrical parameters measured manually by experienced doctors. However, these measurements consume the time and effort of experts to perform repetitive tasks that could be automated, especially with the powerful support of current artificial intelligence techniques. This paper focuses on creation of a decentralized convolutional neural network to precisely measure 12 spinal alignment parameters. Specifically, this method is based on detecting regions of interest with its dimensions that decrease by three orders of magnitude to focus on the necessary region to provide the output as key points. Using these key points, parameters representing spinal alignment are calculated. The quality of the method's performance, which is the consistency of the measurement results with manual measurement, is validated by 30 test cases and shows 10 of 12 parameters with a correlation coefficient > 0.8, with pelvic tilt having the smallest absolute deviation of 1.156°.