TY - JOUR
T1 - Automated triaging of head MRI examinations using convolutional neural networks
AU - Wood, David A.
AU - Kafiabadi, Sina
AU - Al Busaidi, Aisha
AU - Guilhem, Emily
AU - Montvila, Antanas
AU - Agarwal, Siddharth
AU - Lynch, Jeremy
AU - Townend, Matthew
AU - Barker, Gareth
AU - Ourselin, Sebastian
AU - Cole, James H.
AU - Booth, Thomas C.
N1 - 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 203148/Z/16/Z)
Publisher Copyright:
© 2021 D.A. Wood1 et al.
PY - 2022/5
Y1 - 2022/5
N2 - 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 around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in T2-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital (∆AUC ≤ 0.02). A simulation study demonstrated that our 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 hospitals, demonstrating feasibility for use in a clinical triage environment.
AB - 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 around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in T2-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital (∆AUC ≤ 0.02). A simulation study demonstrated that our 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 hospitals, demonstrating feasibility for use in a clinical triage environment.
UR - http://www.scopus.com/inward/record.url?scp=85162852977&partnerID=8YFLogxK
U2 - 10.1016/j.media.2022.102391
DO - 10.1016/j.media.2022.102391
M3 - Conference paper
AN - SCOPUS:85162852977
SN - 1361-8415
VL - 78
SP - 813
EP - 841
JO - Medical Image Analysis
JF - Medical Image Analysis
IS - 102391
T2 - 4th Conference on Medical Imaging with Deep Learning, MIDL 2021
Y2 - 7 July 2021 through 9 July 2021
ER -