Towards a Clinical Decision-Support System for Automating MRI Protocoling

Motivation: We developed a system that performs radiology protocol selection for incoming MRI orders. Goal(s): To enhance MRI protocol selection accuracy and efficiency. We evaluated new models and expanded anatomic/subspeciality coverage compared to a prior body MRI protocol selection system. Approach: A machine learning-driven decision-support system was developed integrating kernel-based, tree-based, boosting, and deep-learning algorithms with an ensemble classifier in 22,524 patients. This system utilizes electronic medical records to predict the top-three likely MRI protocols and their probabilities. Results: A cumulative F1-score of 97.1% for the top-three predicted MRI protocols was obtained in a test set of 3,379 patients. Impact: The proposed system has the potential to improve radiologists’ protocol selection accuracy by notifying them of protocol-case discrepancies due to the individual patient’s conditions, and to enable a decision-support system for greater efficiency in selecting commonly utilized MR protocols.