A Machine Learning System to Automate Body Computed Tomography Protocoling

Abstract

Selection of radiology imaging protocols is a vital step in the radiology workflow as incorrect protocol selection can lead to suboptimal imaging and thereby jeopardize patient health, delay treatments, and/or increase healthcare costs. However, this process is generally thought of as an inefficient use of radiologist's time. We developed a machine learning (ML) system that can predict radiology protocols accurately based on patients' electronic medical record (EMR) data. The system is an ensemble of three decision tree (DT)-based techniques trained to provide protocols for body computed tomography (CT) examinations. The most common 15 CT abdomen protocols were used to tune the models, with the system designed to provide the three most probable predictions for further radiologist revision. Our ensemble classifier, with the F1 score of approximately 83%, outperformed each model with the mean F1 score of approximately 80% in 5-fold cross-validation and performed the best with an F1 score of 95.5% for the top three predictions, surpassing the individual models with F1 scores ranging from 87.6% to 92.9%. In conclusion, the present study demonstrates that ML techniques can predict radiology protocols and identify key classification-dependent features. These models could be leveraged for use as a clinical decision support system to improve radiologists' efficiency.

Keywords: And Radiology Protocol; Boosting Models; Computed Tomography; Decision Tree Models; Decision-Support System; Machine Learning.