Hanging protocol optimization of lumbar spine radiographs with machine learning
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1UPMC Department of Radiology, University of Pittsburgh Medical Center (UPMC) and University of Pittsburgh, 200 Lothrop St., Pittsburgh, PA, 15213, USA. kitamurag@upmc.edu.
Objectives:
The purpose of this study was to determine whether machine learning algorithms can be utilized to optimize the hanging protocol of lumbar spine radiographs. Specifically, we explored whether machine learning models can accurately label lumbar spine views/positions, detect hardware, and rotate the lateral views to straighten the image.
Methods:
We identified 1727 patients with 6988 lumbar spine radiographs. The view (anterior-posterior, right oblique, left oblique, left lateral, right lateral, left lumbosacral or right lumbosacral), hardware (present or not present), dynamic position (neutral, flexion, or extension), and correctional rotation of each radiograph were manually documented by a board-certified radiologist. Various output metrics were calculated, including area under the curve (AUC) for the categorical output models (view, hardware, and dynamic position). For non-binary categories, an all-versus-other technique was utilized designating one category as true and all others as false, allowing for a binary evaluation (e.g., AP vs. non-AP or extension vs. non-extension). For correctional rotation, the degree of rotation required to straighten the lateral spine radiograph was documented. The mean absolute difference was calculated between the ground truth and model-predicted value reported in degrees of rotation. Ensembles of the rotation models were created. We evaluated the rotation models on 3 test dataset splits: only 0 rotation, only non-0 rotation, and all cases.
Results:
The AUC values for the categorical models ranged from 0.985 to 1.000. For the only 0 rotation data, the ensemble combining the absolute minimum value between the 20- and 60-degree models performed best (mean absolute difference of 0.610). For the non-0 rotation data, the ensemble merging the absolute maximum value between the 40- and 160-degree models performed best (mean absolute difference of 4.801). For the all cases split, the ensemble combining the minimum value of the 20- and 40-degree models performed best (mean absolute difference of 3.083).
Conclusion:
Machine learning techniques can be successfully implemented to optimize lumbar spine x-ray hanging protocols by accounting for views, hardware, dynamic position, and rotation correction.
