Diagnosing Optic Neuritis in Neuromyelitis Optica Spectrum Disorders (NMOSD) Using 6 Machine Learning Models with MRI
Neuromyelitis Optica Spectrum Disorders (NMOSD) is an inflammatory disease in the human central nervous system that causes severe optic neuritis (ON). ON is not necessarily present in all NMOSD patients. Thus far, no study has distinguished NMOSD patients with and without ON. Therefore, this study a...
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| description | Neuromyelitis Optica Spectrum Disorders (NMOSD) is an inflammatory disease in the human central nervous system that causes severe optic neuritis (ON). ON is not necessarily present in all NMOSD patients. Thus far, no study has distinguished NMOSD patients with and without ON. Therefore, this study aims to diagnose ON in NMOSD patients using 6 prominent machine learning (ML) models: decision trees, linear discriminant analysis, logistic regression, support vector machines (SVM), Naïve Bayes, and k-nearest-neighbor. This study measured three non-texture (quantitative) features: area, volume, and signal intensity; and five texture (qualitative) features: energy, entropy, homogeneity, contrast, and correlation, of the optic nerves on MR images. This is the first study that used the texture features of the optic nerve for the diagnosis of ON in NMOSD patients. All these features and the age of patients are used for training and testing the ML models. There is a significant difference (
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| doi_str_mv | 10.1007/s42979-024-03363-6 |
| format | Article |
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p
< 0.001) in all the non-texture features between NMOSD patients with and without ON. However, only two texture features (contrast and correlation) show significant differences (
p
< 0.01). All the ML models proved effective in diagnosing ON in NMOSD patients with accuracies between 81.3% and 91.2%. SVM demonstrated the best results with the highest accuracy of 91.2%, sensitivity of 86.1%, specificity of 95.4%, precision of 93.9%, negative predictive value of 89.3%, and F1-score of 89.9%. The permutation feature importance analysis showed that volume is the most important feature for the diagnosis of ON with the SVM model. The ML diagnosis tool for ON is useful for physicians to establish the diagnosis of ON in addition to clinical assessment and conventional visual assessment of MRI.</description><identifier>ISSN: 2661-8907</identifier><identifier>ISSN: 2662-995X</identifier><identifier>EISSN: 2661-8907</identifier><identifier>DOI: 10.1007/s42979-024-03363-6</identifier><language>eng</language><publisher>Singapore: Springer Nature Singapore</publisher><subject>Accuracy ; Aquaporins ; Breast cancer ; Central nervous system ; Computer Imaging ; Computer Science ; Computer Systems Organization and Communication Networks ; COVID-19 ; Data Structures and Information Theory ; Datasets ; Decision trees ; Diagnosis ; Discriminant analysis ; Disease ; Disorders ; Females ; Homogeneity ; Image contrast ; Information Systems and Communication Service ; Machine learning ; Magnetic resonance imaging ; Nerves ; Neuritis ; Optic nerve ; Original Research ; Pattern Recognition and Graphics ; Permutations ; Qualitative analysis ; Research Advancements in Intelligent Computing ; Software Engineering/Programming and Operating Systems ; Support vector machines ; Texture ; Vision</subject><ispartof>SN computer science, 2024-10, Vol.5 (8), p.1005, Article 1005</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1156-61f0ab7cf0191517c9f1a14e6d02db4205ba0e83cf6de1cae51f36ad9d7ec3cf3</cites><orcidid>0000-0003-3877-4434</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s42979-024-03363-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s42979-024-03363-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Feng, Yang</creatorcontrib><creatorcontrib>Chow, Li Sze</creatorcontrib><creatorcontrib>Gowdh, Nadia Muhammad</creatorcontrib><creatorcontrib>Ramli, Norlisah</creatorcontrib><creatorcontrib>Tan, Li Kuo</creatorcontrib><creatorcontrib>Abdullah, Suhailah</creatorcontrib><creatorcontrib>Tiang, Sew Sun</creatorcontrib><title>Diagnosing Optic Neuritis in Neuromyelitis Optica Spectrum Disorders (NMOSD) Using 6 Machine Learning Models with MRI</title><title>SN computer science</title><addtitle>SN COMPUT. SCI</addtitle><description>Neuromyelitis Optica Spectrum Disorders (NMOSD) is an inflammatory disease in the human central nervous system that causes severe optic neuritis (ON). ON is not necessarily present in all NMOSD patients. Thus far, no study has distinguished NMOSD patients with and without ON. Therefore, this study aims to diagnose ON in NMOSD patients using 6 prominent machine learning (ML) models: decision trees, linear discriminant analysis, logistic regression, support vector machines (SVM), Naïve Bayes, and k-nearest-neighbor. This study measured three non-texture (quantitative) features: area, volume, and signal intensity; and five texture (qualitative) features: energy, entropy, homogeneity, contrast, and correlation, of the optic nerves on MR images. This is the first study that used the texture features of the optic nerve for the diagnosis of ON in NMOSD patients. All these features and the age of patients are used for training and testing the ML models. There is a significant difference (
p
< 0.001) in all the non-texture features between NMOSD patients with and without ON. However, only two texture features (contrast and correlation) show significant differences (
p
< 0.01). All the ML models proved effective in diagnosing ON in NMOSD patients with accuracies between 81.3% and 91.2%. SVM demonstrated the best results with the highest accuracy of 91.2%, sensitivity of 86.1%, specificity of 95.4%, precision of 93.9%, negative predictive value of 89.3%, and F1-score of 89.9%. The permutation feature importance analysis showed that volume is the most important feature for the diagnosis of ON with the SVM model. The ML diagnosis tool for ON is useful for physicians to establish the diagnosis of ON in addition to clinical assessment and conventional visual assessment of MRI.</description><subject>Accuracy</subject><subject>Aquaporins</subject><subject>Breast cancer</subject><subject>Central nervous system</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer Systems Organization and Communication Networks</subject><subject>COVID-19</subject><subject>Data Structures and Information Theory</subject><subject>Datasets</subject><subject>Decision trees</subject><subject>Diagnosis</subject><subject>Discriminant analysis</subject><subject>Disease</subject><subject>Disorders</subject><subject>Females</subject><subject>Homogeneity</subject><subject>Image contrast</subject><subject>Information Systems and Communication Service</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Nerves</subject><subject>Neuritis</subject><subject>Optic nerve</subject><subject>Original Research</subject><subject>Pattern Recognition and Graphics</subject><subject>Permutations</subject><subject>Qualitative analysis</subject><subject>Research Advancements in Intelligent Computing</subject><subject>Software Engineering/Programming and Operating Systems</subject><subject>Support vector machines</subject><subject>Texture</subject><subject>Vision</subject><issn>2661-8907</issn><issn>2662-995X</issn><issn>2661-8907</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kE9Lw0AQxRdRsGi_gKcFL3qIzu4mm-YorX8KTQvWnpftZtJuaZO4myD99qaJoCdPM7x57w38CLlh8MAA4kcf8iROAuBhAEJIEcgzMuBSsmCUQHz-Z78kQ-93AMAjCEMZDUgzsXpTlN4WG7qoamvoHBtna-upLbq9PBxx3wndXdNlhaZ2zYFOrC9dhs7Tu3m6WE7u6arrkTTVZmsLpDPUrjhJaZnh3tMvW29p-j69Jhe53nsc_swrsnp5_hi_BbPF63T8NAsMY5EMJMtBr2OTA0tYxGKT5EyzEGUGPFuHHKK1BhwJk8sMmdEYsVxInSVZjKZVxRW57XsrV3426Gu1KxtXtC-VYJyHMIKYty7eu4wrvXeYq8rZg3ZHxUCdCKuesGoJq46wkm1I9CHfmosNut_qf1LfWH1-eg</recordid><startdate>20241030</startdate><enddate>20241030</enddate><creator>Feng, Yang</creator><creator>Chow, Li Sze</creator><creator>Gowdh, Nadia Muhammad</creator><creator>Ramli, Norlisah</creator><creator>Tan, Li Kuo</creator><creator>Abdullah, Suhailah</creator><creator>Tiang, Sew Sun</creator><general>Springer Nature Singapore</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><orcidid>https://orcid.org/0000-0003-3877-4434</orcidid></search><sort><creationdate>20241030</creationdate><title>Diagnosing Optic Neuritis in Neuromyelitis Optica Spectrum Disorders (NMOSD) Using 6 Machine Learning Models with MRI</title><author>Feng, Yang ; Chow, Li Sze ; Gowdh, Nadia Muhammad ; Ramli, Norlisah ; Tan, Li Kuo ; Abdullah, Suhailah ; Tiang, Sew Sun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1156-61f0ab7cf0191517c9f1a14e6d02db4205ba0e83cf6de1cae51f36ad9d7ec3cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Aquaporins</topic><topic>Breast cancer</topic><topic>Central nervous system</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer Systems Organization and Communication Networks</topic><topic>COVID-19</topic><topic>Data Structures and Information Theory</topic><topic>Datasets</topic><topic>Decision trees</topic><topic>Diagnosis</topic><topic>Discriminant analysis</topic><topic>Disease</topic><topic>Disorders</topic><topic>Females</topic><topic>Homogeneity</topic><topic>Image contrast</topic><topic>Information Systems and Communication Service</topic><topic>Machine learning</topic><topic>Magnetic resonance imaging</topic><topic>Nerves</topic><topic>Neuritis</topic><topic>Optic nerve</topic><topic>Original Research</topic><topic>Pattern Recognition and Graphics</topic><topic>Permutations</topic><topic>Qualitative analysis</topic><topic>Research Advancements in Intelligent Computing</topic><topic>Software Engineering/Programming and Operating Systems</topic><topic>Support vector machines</topic><topic>Texture</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Feng, Yang</creatorcontrib><creatorcontrib>Chow, Li Sze</creatorcontrib><creatorcontrib>Gowdh, Nadia Muhammad</creatorcontrib><creatorcontrib>Ramli, Norlisah</creatorcontrib><creatorcontrib>Tan, Li Kuo</creatorcontrib><creatorcontrib>Abdullah, Suhailah</creatorcontrib><creatorcontrib>Tiang, Sew Sun</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>SN computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Feng, Yang</au><au>Chow, Li Sze</au><au>Gowdh, Nadia Muhammad</au><au>Ramli, Norlisah</au><au>Tan, Li Kuo</au><au>Abdullah, Suhailah</au><au>Tiang, Sew Sun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diagnosing Optic Neuritis in Neuromyelitis Optica Spectrum Disorders (NMOSD) Using 6 Machine Learning Models with MRI</atitle><jtitle>SN computer science</jtitle><stitle>SN COMPUT. SCI</stitle><date>2024-10-30</date><risdate>2024</risdate><volume>5</volume><issue>8</issue><spage>1005</spage><pages>1005-</pages><artnum>1005</artnum><issn>2661-8907</issn><issn>2662-995X</issn><eissn>2661-8907</eissn><abstract>Neuromyelitis Optica Spectrum Disorders (NMOSD) is an inflammatory disease in the human central nervous system that causes severe optic neuritis (ON). ON is not necessarily present in all NMOSD patients. Thus far, no study has distinguished NMOSD patients with and without ON. Therefore, this study aims to diagnose ON in NMOSD patients using 6 prominent machine learning (ML) models: decision trees, linear discriminant analysis, logistic regression, support vector machines (SVM), Naïve Bayes, and k-nearest-neighbor. This study measured three non-texture (quantitative) features: area, volume, and signal intensity; and five texture (qualitative) features: energy, entropy, homogeneity, contrast, and correlation, of the optic nerves on MR images. This is the first study that used the texture features of the optic nerve for the diagnosis of ON in NMOSD patients. All these features and the age of patients are used for training and testing the ML models. There is a significant difference (
p
< 0.001) in all the non-texture features between NMOSD patients with and without ON. However, only two texture features (contrast and correlation) show significant differences (
p
< 0.01). All the ML models proved effective in diagnosing ON in NMOSD patients with accuracies between 81.3% and 91.2%. SVM demonstrated the best results with the highest accuracy of 91.2%, sensitivity of 86.1%, specificity of 95.4%, precision of 93.9%, negative predictive value of 89.3%, and F1-score of 89.9%. The permutation feature importance analysis showed that volume is the most important feature for the diagnosis of ON with the SVM model. The ML diagnosis tool for ON is useful for physicians to establish the diagnosis of ON in addition to clinical assessment and conventional visual assessment of MRI.</abstract><cop>Singapore</cop><pub>Springer Nature Singapore</pub><doi>10.1007/s42979-024-03363-6</doi><orcidid>https://orcid.org/0000-0003-3877-4434</orcidid></addata></record> |
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| subjects | Accuracy Aquaporins Breast cancer Central nervous system Computer Imaging Computer Science Computer Systems Organization and Communication Networks COVID-19 Data Structures and Information Theory Datasets Decision trees Diagnosis Discriminant analysis Disease Disorders Females Homogeneity Image contrast Information Systems and Communication Service Machine learning Magnetic resonance imaging Nerves Neuritis Optic nerve Original Research Pattern Recognition and Graphics Permutations Qualitative analysis Research Advancements in Intelligent Computing Software Engineering/Programming and Operating Systems Support vector machines Texture Vision |
| title | Diagnosing Optic Neuritis in Neuromyelitis Optica Spectrum Disorders (NMOSD) Using 6 Machine Learning Models with MRI |
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