Estimation of patient's angle from skull radiographs using deep learning.

Abstract

Skull radiography, an assessment method for initial diagnosis and post-operative follow-up, requires substantial retaking of various types of radiographs. During retaking, a radiologic technologist estimates a patient's rotation angle from the radiograph by comprehending the relationship between the radiograph and the patient's angle for adequate assessment, which requires extensive experience. To develop and test a new deep learning model or method to automatically estimate patient's angle from radiographs. The patient's position is assessed using deep learning to estimate their angle from skull radiographs. Skull radiographs are simulated using two-dimensional projections from head computed tomography images and used as input data to estimate the patient's angle, using deep learning under supervised training. A residual neural network model is used where the rectified linear unit is changed to a parametric rectified linear unit, and dropout is added. The patient's angle is estimated in the lateral and superior-inferior directions. Applying this new deep learning model, the estimation errors are 0.56±0.36° and 0.72±0.52° in the lateral and superior-inferior angles, respectively. These findings suggest that a patient's angle can be accurately estimated from a radiograph using a deep learning model leading to reduce retaking time, and then used to facilitate skull radiography.