Machine Learning and Deep Learning Models for Automated Protocoling of Emergency Brain MRI Using Text from Clinical...

Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emergency brain MRI referrals from January 2016 to January 2019, two neuroradiologists labeled the imaging protocol and use of contrast agent as the reference standard. Three machine learning algorithms (naive Bayes, support vector machine, and XGBoost) and two pretrained deep learning models (Finnish bidirectional encoder representations from transformers [BERT] and generative pretrained transformer [GPT]-3.5 [GPT-3.5 Turbo; Open AI]) were developed to predict the MRI protocol and need for a contrast agent. Each model was trained with three datasets (100% of training data, 50% of training data, and 50% plus augmented training data). Prediction accuracy was assessed with a test set. Results The GPT-3.5 models trained with 100% of the training data performed best in both tasks, achieving an accuracy of 84% (95% CI: 80, 88) for the correct protocol and 91% (95% CI: 88, 94) for the contrast agent. BERT had an accuracy of 78% (95% CI: 74, 82) for the protocol and 89% (95% CI: 86, 92) for the contrast agent. The best machine learning model in the protocol task was XGBoost (accuracy, 78%; 95% CI: 73, 82), and the best machine learning models in the contrast agent task were support vector machine and XGBoost (accuracy, 88%; 95% CI: 84, 91 for both). The accuracies of two nonneuroradiologists were 80%-83% in the protocol task and 89%-91% in the contrast medium task. Conclusion Machine learning and deep learning models demonstrated high performance in automatic protocoling of emergency brain MRI scans based on text from clinical referrals. Keywords: Natural Language Processing, Automatic Protocoling, Deep Learning, Machine Learning, Emergency Brain MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Strotzer in this issue.