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

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Heidi J Huhtanen, Mikko J Nyman, Antti Karlsson, Jussi Hirvonen
{"title":"Machine Learning and Deep Learning Models for Automated Protocoling of Emergency Brain MRI Using Text from Clinical Referrals.","authors":"Heidi J Huhtanen, Mikko J Nyman, Antti Karlsson, Jussi Hirvonen","doi":"10.1148/ryai.230620","DOIUrl":null,"url":null,"abstract":"<p><p><i>\"Just Accepted\" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> 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 (Naïve Bayes, support vector machine, and XGBoost) and two pretrained deep learning models (Finnish BERT and GPT-3.5) were developed to predict the MRI protocol and need for contrast agent. Each model was trained with three datasets (100% of training data, 50% of training data, and 50% + augmented training data). Prediction accuracy was assessed with test set. Results The GPT-3.5 models trained with 100% of the training data performed best in both tasks, achieving accuracy of 84% (95% CI: 80%-88%) for the correct protocol and 91% (95% CI: 88%-94%) for contrast. BERT had an accuracy of 78% (95% CI: 74%-82%) for the protocol and 89% (95% CI: 86%-92%) for contrast. The best machine learning model in the protocol task was XGBoost (accuracy 78% [95% CI: 73%-82%]), and in the contrast agent task 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 emergency brain MRI scans based on text from clinical referrals. Published under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230620"},"PeriodicalIF":8.1000,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology-Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1148/ryai.230620","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. 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 (Naïve Bayes, support vector machine, and XGBoost) and two pretrained deep learning models (Finnish BERT and GPT-3.5) were developed to predict the MRI protocol and need for contrast agent. Each model was trained with three datasets (100% of training data, 50% of training data, and 50% + augmented training data). Prediction accuracy was assessed with test set. Results The GPT-3.5 models trained with 100% of the training data performed best in both tasks, achieving accuracy of 84% (95% CI: 80%-88%) for the correct protocol and 91% (95% CI: 88%-94%) for contrast. BERT had an accuracy of 78% (95% CI: 74%-82%) for the protocol and 89% (95% CI: 86%-92%) for contrast. The best machine learning model in the protocol task was XGBoost (accuracy 78% [95% CI: 73%-82%]), and in the contrast agent task 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 emergency brain MRI scans based on text from clinical referrals. Published under a CC BY 4.0 license.

使用临床转诊文本的紧急脑MRI自动协议的机器学习和深度学习模型。
“刚刚接受”的论文经过了全面的同行评审,并已被接受发表在《放射学:人工智能》杂志上。这篇文章将经过编辑,布局和校样审查,然后在其最终版本出版。请注意,在最终编辑文章的制作过程中,可能会发现可能影响内容的错误。目的开发和评估基于机器学习和深度学习的模型,用于基于临床转诊文本的紧急脑MRI扫描的自动处理。在2016年1月至2019年1月的1953例急诊脑MRI患者的单机构回顾性研究中,两名神经放射科医生将成像方案和造影剂的使用作为参考标准。开发了三种机器学习算法(Naïve Bayes、支持向量机和XGBoost)和两种预训练深度学习模型(芬兰BERT和GPT-3.5)来预测MRI方案和造影剂需求。每个模型使用三个数据集(100%的训练数据,50%的训练数据和50% +增强训练数据)进行训练。用测试集评估预测精度。使用100%训练数据训练的GPT-3.5模型在两项任务中都表现最好,正确方案的准确率为84% (95% CI: 80%-88%),对比方案的准确率为91% (95% CI: 88%-94%)。BERT对方案的准确率为78% (95% CI: 74%-82%),对比的准确率为89% (95% CI: 86%-92%)。协议任务中最好的机器学习模型是XGBoost(准确率78% [95% CI: 73%-82%]),造影剂任务中支持向量机和XGBoost(两者的准确率88% [95% CI: 84%-91%])。两名非神经放射学家在方案任务中的准确率为80%-83%,在造影剂任务中的准确率为89%-91%。结论:机器学习和深度学习模型在基于临床转诊文本的紧急脑MRI扫描自动处理中表现出高性能。在CC BY 4.0许可下发布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信