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Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN

Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient’s lumbar spine. Currently, the versatility and ac...

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Autores principales: Shahzadi, Turrnum, Ali, Muhammad Usman, Majeed, Fiaz, Sana, Muhammad Usman, Diaz, Raquel Martínez, Samad, Md Abdus, Ashraf, Imran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10529899/
https://www.ncbi.nlm.nih.gov/pubmed/37761342
http://dx.doi.org/10.3390/diagnostics13182975
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author Shahzadi, Turrnum
Ali, Muhammad Usman
Majeed, Fiaz
Sana, Muhammad Usman
Diaz, Raquel Martínez
Samad, Md Abdus
Ashraf, Imran
author_facet Shahzadi, Turrnum
Ali, Muhammad Usman
Majeed, Fiaz
Sana, Muhammad Usman
Diaz, Raquel Martínez
Samad, Md Abdus
Ashraf, Imran
author_sort Shahzadi, Turrnum
collection PubMed
description Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient’s lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets—multi-ROI and single-ROI—are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach.
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spelling pubmed-105298992023-09-28 Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN Shahzadi, Turrnum Ali, Muhammad Usman Majeed, Fiaz Sana, Muhammad Usman Diaz, Raquel Martínez Samad, Md Abdus Ashraf, Imran Diagnostics (Basel) Article Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient’s lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets—multi-ROI and single-ROI—are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach. MDPI 2023-09-18 /pmc/articles/PMC10529899/ /pubmed/37761342 http://dx.doi.org/10.3390/diagnostics13182975 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shahzadi, Turrnum
Ali, Muhammad Usman
Majeed, Fiaz
Sana, Muhammad Usman
Diaz, Raquel Martínez
Samad, Md Abdus
Ashraf, Imran
Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN
title Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN
title_full Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN
title_fullStr Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN
title_full_unstemmed Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN
title_short Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN
title_sort nerve root compression analysis to find lumbar spine stenosis on mri using cnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10529899/
https://www.ncbi.nlm.nih.gov/pubmed/37761342
http://dx.doi.org/10.3390/diagnostics13182975
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