To visualize widening of the intervertebral discspace (IDS), X-rays areirradiated tangentially on the articular surface. However, accurately estimatingthe deflection angle of the x-ray tube is difficult. Therefore, we classifiedthe standing lateral lumbar spine images into 5 types by a Deep ConvolutionalNeural Network, determined the deflection angle of the X-ray tube for moreefficient visualization of the extension of the IDS in the standing frontallumbar spine images. In the lumbar lateral images of 500 patients, the x-raytube deflection angle was measured manually for each intervertebral space, and 1723 regions cut out into 256×256 regions per vertebra wereused．Data augmentation increased the data to 3795 regions. The accuracy, precision and recall were 83.0 %, 84.1% and 83%, respectively, and the f-value was 83.3%, resulting in a relatively highclassification accuracy. Many patientsare already having lower back pain, requiring them to shift from the lateral tofrontal body positions swiftly. For this reason, using deep learning wouldenable taking the IDS measurement in a short time, thereby reducing burden onthe patient and improving the imaging flow.
