A deep learning model for detection of cervical spinal cord compression in MRI scans

Abstract Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volume of medical imaging data and provide ini...

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Autores principales:	Zamir Merali, Justin Z. Wang, Jetan H. Badhiwala, Christopher D. Witiw, Jefferson R. Wilson, Michael G. Fehlings
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7f7756e69ee6443a8a43b04b5b9616be
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spelling	oai:doaj.org-article:7f7756e69ee6443a8a43b04b5b9616be2021-12-02T15:52:47ZA deep learning model for detection of cervical spinal cord compression in MRI scans10.1038/s41598-021-89848-32045-2322https://doaj.org/article/7f7756e69ee6443a8a43b04b5b9616be2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89848-3https://doaj.org/toc/2045-2322Abstract Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volume of medical imaging data and provide initial interpretation of images gathered in a primary-care setting. We aimed to develop and validate a deep learning model for detection of cervical spinal cord compression in MRI scans. Patients undergoing surgery for DCM as a part of the AO Spine CSM-NA or CSM-I prospective cohort studies were included in our study. Patients were divided into a training/validation or holdout dataset. Images were labelled by two specialist physicians. We trained a deep convolutional neural network using images from the training/validation dataset and assessed model performance on the holdout dataset. The training/validation cohort included 201 patients with 6588 images and the holdout dataset included 88 patients with 2991 images. On the holdout dataset the deep learning model achieved an overall AUC of 0.94, sensitivity of 0.88, specificity of 0.89, and f1-score of 0.82. This model could improve the efficiency and objectivity of the interpretation of cervical spine MRI scans.Zamir MeraliJustin Z. WangJetan H. BadhiwalaChristopher D. WitiwJefferson R. WilsonMichael G. FehlingsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zamir Merali Justin Z. Wang Jetan H. Badhiwala Christopher D. Witiw Jefferson R. Wilson Michael G. Fehlings A deep learning model for detection of cervical spinal cord compression in MRI scans
description	Abstract Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volume of medical imaging data and provide initial interpretation of images gathered in a primary-care setting. We aimed to develop and validate a deep learning model for detection of cervical spinal cord compression in MRI scans. Patients undergoing surgery for DCM as a part of the AO Spine CSM-NA or CSM-I prospective cohort studies were included in our study. Patients were divided into a training/validation or holdout dataset. Images were labelled by two specialist physicians. We trained a deep convolutional neural network using images from the training/validation dataset and assessed model performance on the holdout dataset. The training/validation cohort included 201 patients with 6588 images and the holdout dataset included 88 patients with 2991 images. On the holdout dataset the deep learning model achieved an overall AUC of 0.94, sensitivity of 0.88, specificity of 0.89, and f1-score of 0.82. This model could improve the efficiency and objectivity of the interpretation of cervical spine MRI scans.
format	article
author	Zamir Merali Justin Z. Wang Jetan H. Badhiwala Christopher D. Witiw Jefferson R. Wilson Michael G. Fehlings
author_facet	Zamir Merali Justin Z. Wang Jetan H. Badhiwala Christopher D. Witiw Jefferson R. Wilson Michael G. Fehlings
author_sort	Zamir Merali
title	A deep learning model for detection of cervical spinal cord compression in MRI scans
title_short	A deep learning model for detection of cervical spinal cord compression in MRI scans
title_full	A deep learning model for detection of cervical spinal cord compression in MRI scans
title_fullStr	A deep learning model for detection of cervical spinal cord compression in MRI scans
title_full_unstemmed	A deep learning model for detection of cervical spinal cord compression in MRI scans
title_sort	deep learning model for detection of cervical spinal cord compression in mri scans
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7f7756e69ee6443a8a43b04b5b9616be
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A deep learning model for detection of cervical spinal cord compression in MRI scans

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