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Deep learning models for triaging hospital head MRI examinations

Research output: Contribution to journalArticlepeer-review

David A Wood, Sina Kafiabadi, Ayisha Al Busaidi, Emily Guilhem, Antanas Montvila, Jeremy Lynch, Matthew Townend, Siddharth Agarwal, Asif Mazumder, Gareth J Barker, Sebastien Ourselin, James H Cole, Thomas C Booth

Original languageEnglish
Article number102391
Pages (from-to)102391
JournalMedical Image Analysis
Volume78
Early online date12 Feb 2022
DOIs
Accepted/In press13 Jan 2022
E-pub ahead of print12 Feb 2022
PublishedMay 2022

Bibliographical note

Funding Information: This work was supported by the Royal College of Radiologists , King's College Hospital Research and Innovation, King's Health Partners Challenge Fund, NVIDIA (through the unrestricted use of a GPU obtained in a competition), and the Wellcome/Engineering and Physical Sciences Research Council center for Medical Engineering (WT 203,148/Z/16/Z). Publisher Copyright: © 2022

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  • Wood_2021_MIA_final_clean

    Wood_2021_MIA_final_clean.docx, 4.81 MB, application/vnd.openxmlformats-officedocument.wordprocessingml.document

    Uploaded date:02 Feb 2022

    Version:Accepted author manuscript

  • 1-s2.0-S1361841522000433-main

    1_s2.0_S1361841522000433_main.pdf, 6.14 MB, application/pdf

    Uploaded date:23 Feb 2022

    Version:Final published version

    Licence:CC BY

    1361-8415/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

King's Authors

Abstract

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans in recent years. For many neurological conditions, this delay can result in poorer patient outcomes and inflated healthcare costs. Potentially, computer vision models could help reduce reporting times for abnormal examinations by flagging abnormalities at the time of imaging, allowing radiology departments to prioritise limited resources into reporting these scans first. To date, however, the difficulty of obtaining large, clinically-representative labelled datasets has been a bottleneck to model development. In this work, we present a deep learning framework, based on convolutional neural networks, for detecting clinically-relevant abnormalities in minimally processed, hospital-grade axial T2-weighted and axial diffusion-weighted head MRI scans. The models were trained at scale using a Transformer-based neuroradiology report classifier to generate a labelled dataset of 70,206 examinations from two large UK hospital networks, and demonstrate fast (< 5 s), accurate (area under the receiver operating characteristic curve (AUC) > 0.9), and interpretable classification, with good generalisability between hospitals (ΔAUC ≤ 0.02). Through a simulation study we show that our best model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospital networks, demonstrating feasibility for use in a clinical triage environment.

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