Estimation of patient’s angle from skull radiographs using deep learning

Abstract

BACKGROUND:

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.

OBJECTIVE:

To develop and test a new deep learning model or method to automatically estimate patient’s angle from radiographs.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.