Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer’s Disease

The 3D tortuosity determined in several brain areas is proposed as a new morphological biomarker (BM) to be considered in early detection of Alzheimer’s disease (AD). It is measured using the sum of angles method and it has proven to be sensitive to anatomical changes that appear in gray and white m...

Full description

Saved in:
Bibliographic Details
Published in:Computational intelligence and neuroscience 2020, Vol.2020 (2020), p.1-11
Main Authors: Medina-Bañuelos, Verónica, Rojas-Saavedra, Karla C., Pérez-González, Jorge, Barbará-Morales, Eduardo
Format: Article
Language:English
Subjects:
Advertising executives
Aged
Algorithms
Alzheimer Disease - classification
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - pathology
Alzheimer's disease
Artificial intelligence
Automatic classification
Biomarkers
Brain
Brain - diagnostic imaging
Brain - pathology
Brain research
Cerebrospinal fluid
Cognitive ability
Dementia
Diagnosis
Female
Humans
Image classification
Image Interpretation, Computer-Assisted - methods
Machine learning
Magnetic Resonance Imaging
Male
Measurement
Medical imaging
Morphology
Neurodegenerative diseases
Parameters
Pharmaceutical industry
Population studies
Statistical analysis
Statistical methods
Substantia alba
Support vector machines
Tau protein
Tortuosity
β-Amyloid
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c499t-18ec3f948eb2b2a2f8b7b3df2620395be2e2d32d0e6a5c18e4e2e268216bb5f93
cites cdi_FETCH-LOGICAL-c499t-18ec3f948eb2b2a2f8b7b3df2620395be2e2d32d0e6a5c18e4e2e268216bb5f93
container_end_page 11
container_issue 2020
container_start_page 1
container_title Computational intelligence and neuroscience
container_volume 2020
creator Medina-Bañuelos, Verónica
Rojas-Saavedra, Karla C.
Pérez-González, Jorge
Barbará-Morales, Eduardo
description The 3D tortuosity determined in several brain areas is proposed as a new morphological biomarker (BM) to be considered in early detection of Alzheimer’s disease (AD). It is measured using the sum of angles method and it has proven to be sensitive to anatomical changes that appear in gray and white matter and temporal and parietal lobes during mild cognitive impairment (MCI). Statistical analysis showed significant differences (p
doi_str_mv 10.1155/2020/4041832
format article
fullrecord <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7204386</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A621086040</galeid><sourcerecordid>A621086040</sourcerecordid><originalsourceid>FETCH-LOGICAL-c499t-18ec3f948eb2b2a2f8b7b3df2620395be2e2d32d0e6a5c18e4e2e268216bb5f93</originalsourceid><addsrcrecordid>eNqNkk1vFCEYxydGY-vqzbMh8WKia3kZ2JmLybq2atLGg_VMGOahw4YZWmDa1MTEr-HX85OUya7jy8lwgMCPHzz8KYqnBL8mhPMjiik-KnFJKkbvFYdEVKslpyt2fx4LflA8inGLMV9xTB8WB4yWmNO6PCy-HV8rN6pk_YC8QW-DsgM69yGNPtp0i85AxTFAD0NCxgeUOkDrMfk-b9HobHTJ9r5VDm2citEaq2fX57HZgk4R3djUobX72oHtIfz8_iOidzZmMTwuHhjlIjzZ94viy8nx-ebD8vTT-4-b9elSl3WdlqQCzUxdVtDQhipqqmbVsNZQQTGreQMUaMtoi0EorjNdTjOiokQ0DTc1WxRvdt7Lsemh1bmaoJy8DLZX4VZ6ZeXfK4Pt5IW_liuKS1aJLHixFwR_NUJMsrdRg3NqAD9Gmd-TTU1MZz3_B936MQy5PEkZp7wWPDtn6kI5kHYwPp-rJ6lcC0pwJXBWLopXO0oHH2MAM1-ZYDmlL6f05T79jD_7s8wZ_hV3Bl7ugM4Orbqx_6mDzIBRv2nCqvyT2B1wo8NQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2352596504</pqid></control><display><type>article</type><title>Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer’s Disease</title><source>Wiley Online Library Open Access</source><source>Publicly Available Content Database</source><creator>Medina-Bañuelos, Verónica ; Rojas-Saavedra, Karla C. ; Pérez-González, Jorge ; Barbará-Morales, Eduardo</creator><contributor>Travieso-González, Carlos M. ; Carlos M Travieso-González</contributor><creatorcontrib>Medina-Bañuelos, Verónica ; Rojas-Saavedra, Karla C. ; Pérez-González, Jorge ; Barbará-Morales, Eduardo ; Travieso-González, Carlos M. ; Carlos M Travieso-González</creatorcontrib><description>The 3D tortuosity determined in several brain areas is proposed as a new morphological biomarker (BM) to be considered in early detection of Alzheimer’s disease (AD). It is measured using the sum of angles method and it has proven to be sensitive to anatomical changes that appear in gray and white matter and temporal and parietal lobes during mild cognitive impairment (MCI). Statistical analysis showed significant differences (p&lt;0.05) between tortuosity indices determined for healthy controls (HC) vs. MCI and HC vs. AD in most of the analyzed structures. Other clinically used BMs have also been incorporated in the analysis: beta-amyloid and tau protein CSF and plasma concentrations, as well as other image-extracted parameters. A classification strategy using random forest (RF) algorithms was implemented to discriminate between three samples of the studied populations, selected from the ADNI database. Classification rates considering only image-extracted parameters show an increase of 9.17%, when tortuosity is incorporated. An enhancement of 1.67% is obtained when BMs measured from several modalities are combined with tortuosity.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2020/4041832</identifier><identifier>PMID: 32405294</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Advertising executives ; Aged ; Algorithms ; Alzheimer Disease - classification ; Alzheimer Disease - diagnostic imaging ; Alzheimer Disease - pathology ; Alzheimer's disease ; Artificial intelligence ; Automatic classification ; Biomarkers ; Brain ; Brain - diagnostic imaging ; Brain - pathology ; Brain research ; Cerebrospinal fluid ; Cognitive ability ; Dementia ; Diagnosis ; Female ; Humans ; Image classification ; Image Interpretation, Computer-Assisted - methods ; Machine learning ; Magnetic Resonance Imaging ; Male ; Measurement ; Medical imaging ; Morphology ; Neurodegenerative diseases ; Parameters ; Pharmaceutical industry ; Population studies ; Statistical analysis ; Statistical methods ; Substantia alba ; Support vector machines ; Tau protein ; Tortuosity ; β-Amyloid</subject><ispartof>Computational intelligence and neuroscience, 2020, Vol.2020 (2020), p.1-11</ispartof><rights>Copyright © 2020 Eduardo Barbará-Morales et al.</rights><rights>COPYRIGHT 2020 John Wiley &amp; Sons, Inc.</rights><rights>Copyright © 2020 Eduardo Barbará-Morales et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2020 Eduardo Barbará-Morales et al. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-18ec3f948eb2b2a2f8b7b3df2620395be2e2d32d0e6a5c18e4e2e268216bb5f93</citedby><cites>FETCH-LOGICAL-c499t-18ec3f948eb2b2a2f8b7b3df2620395be2e2d32d0e6a5c18e4e2e268216bb5f93</cites><orcidid>0000-0001-6696-7618 ; 0000-0001-8072-3927 ; 0000-0002-4069-4268</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2352596504/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2352596504?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,4024,25753,27923,27924,27925,37012,37013,44590,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32405294$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Travieso-González, Carlos M.</contributor><contributor>Carlos M Travieso-González</contributor><creatorcontrib>Medina-Bañuelos, Verónica</creatorcontrib><creatorcontrib>Rojas-Saavedra, Karla C.</creatorcontrib><creatorcontrib>Pérez-González, Jorge</creatorcontrib><creatorcontrib>Barbará-Morales, Eduardo</creatorcontrib><title>Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer’s Disease</title><title>Computational intelligence and neuroscience</title><addtitle>Comput Intell Neurosci</addtitle><description>The 3D tortuosity determined in several brain areas is proposed as a new morphological biomarker (BM) to be considered in early detection of Alzheimer’s disease (AD). It is measured using the sum of angles method and it has proven to be sensitive to anatomical changes that appear in gray and white matter and temporal and parietal lobes during mild cognitive impairment (MCI). Statistical analysis showed significant differences (p&lt;0.05) between tortuosity indices determined for healthy controls (HC) vs. MCI and HC vs. AD in most of the analyzed structures. Other clinically used BMs have also been incorporated in the analysis: beta-amyloid and tau protein CSF and plasma concentrations, as well as other image-extracted parameters. A classification strategy using random forest (RF) algorithms was implemented to discriminate between three samples of the studied populations, selected from the ADNI database. Classification rates considering only image-extracted parameters show an increase of 9.17%, when tortuosity is incorporated. An enhancement of 1.67% is obtained when BMs measured from several modalities are combined with tortuosity.</description><subject>Advertising executives</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Alzheimer Disease - classification</subject><subject>Alzheimer Disease - diagnostic imaging</subject><subject>Alzheimer Disease - pathology</subject><subject>Alzheimer's disease</subject><subject>Artificial intelligence</subject><subject>Automatic classification</subject><subject>Biomarkers</subject><subject>Brain</subject><subject>Brain - diagnostic imaging</subject><subject>Brain - pathology</subject><subject>Brain research</subject><subject>Cerebrospinal fluid</subject><subject>Cognitive ability</subject><subject>Dementia</subject><subject>Diagnosis</subject><subject>Female</subject><subject>Humans</subject><subject>Image classification</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Machine learning</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Measurement</subject><subject>Medical imaging</subject><subject>Morphology</subject><subject>Neurodegenerative diseases</subject><subject>Parameters</subject><subject>Pharmaceutical industry</subject><subject>Population studies</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Substantia alba</subject><subject>Support vector machines</subject><subject>Tau protein</subject><subject>Tortuosity</subject><subject>β-Amyloid</subject><issn>1687-5265</issn><issn>1687-5273</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNkk1vFCEYxydGY-vqzbMh8WKia3kZ2JmLybq2atLGg_VMGOahw4YZWmDa1MTEr-HX85OUya7jy8lwgMCPHzz8KYqnBL8mhPMjiik-KnFJKkbvFYdEVKslpyt2fx4LflA8inGLMV9xTB8WB4yWmNO6PCy-HV8rN6pk_YC8QW-DsgM69yGNPtp0i85AxTFAD0NCxgeUOkDrMfk-b9HobHTJ9r5VDm2citEaq2fX57HZgk4R3djUobX72oHtIfz8_iOidzZmMTwuHhjlIjzZ94viy8nx-ebD8vTT-4-b9elSl3WdlqQCzUxdVtDQhipqqmbVsNZQQTGreQMUaMtoi0EorjNdTjOiokQ0DTc1WxRvdt7Lsemh1bmaoJy8DLZX4VZ6ZeXfK4Pt5IW_liuKS1aJLHixFwR_NUJMsrdRg3NqAD9Gmd-TTU1MZz3_B936MQy5PEkZp7wWPDtn6kI5kHYwPp-rJ6lcC0pwJXBWLopXO0oHH2MAM1-ZYDmlL6f05T79jD_7s8wZ_hV3Bl7ugM4Orbqx_6mDzIBRv2nCqvyT2B1wo8NQ</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Medina-Bañuelos, Verónica</creator><creator>Rojas-Saavedra, Karla C.</creator><creator>Pérez-González, Jorge</creator><creator>Barbará-Morales, Eduardo</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>John Wiley &amp; Sons, Inc</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QF</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>7X7</scope><scope>7XB</scope><scope>8AL</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-6696-7618</orcidid><orcidid>https://orcid.org/0000-0001-8072-3927</orcidid><orcidid>https://orcid.org/0000-0002-4069-4268</orcidid></search><sort><creationdate>2020</creationdate><title>Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer’s Disease</title><author>Medina-Bañuelos, Verónica ; Rojas-Saavedra, Karla C. ; Pérez-González, Jorge ; Barbará-Morales, Eduardo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c499t-18ec3f948eb2b2a2f8b7b3df2620395be2e2d32d0e6a5c18e4e2e268216bb5f93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Advertising executives</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Alzheimer Disease - classification</topic><topic>Alzheimer Disease - diagnostic imaging</topic><topic>Alzheimer Disease - pathology</topic><topic>Alzheimer's disease</topic><topic>Artificial intelligence</topic><topic>Automatic classification</topic><topic>Biomarkers</topic><topic>Brain</topic><topic>Brain - diagnostic imaging</topic><topic>Brain - pathology</topic><topic>Brain research</topic><topic>Cerebrospinal fluid</topic><topic>Cognitive ability</topic><topic>Dementia</topic><topic>Diagnosis</topic><topic>Female</topic><topic>Humans</topic><topic>Image classification</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Machine learning</topic><topic>Magnetic Resonance Imaging</topic><topic>Male</topic><topic>Measurement</topic><topic>Medical imaging</topic><topic>Morphology</topic><topic>Neurodegenerative diseases</topic><topic>Parameters</topic><topic>Pharmaceutical industry</topic><topic>Population studies</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Substantia alba</topic><topic>Support vector machines</topic><topic>Tau protein</topic><topic>Tortuosity</topic><topic>β-Amyloid</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Medina-Bañuelos, Verónica</creatorcontrib><creatorcontrib>Rojas-Saavedra, Karla C.</creatorcontrib><creatorcontrib>Pérez-González, Jorge</creatorcontrib><creatorcontrib>Barbará-Morales, Eduardo</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aluminium Industry Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>ProQuest_Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Database‎ (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East &amp; Africa Database</collection><collection>ProQuest Central</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer science database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest Biological Science Collection</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest Biological Science Journals</collection><collection>Engineering Database</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Computational intelligence and neuroscience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Medina-Bañuelos, Verónica</au><au>Rojas-Saavedra, Karla C.</au><au>Pérez-González, Jorge</au><au>Barbará-Morales, Eduardo</au><au>Travieso-González, Carlos M.</au><au>Carlos M Travieso-González</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer’s Disease</atitle><jtitle>Computational intelligence and neuroscience</jtitle><addtitle>Comput Intell Neurosci</addtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>1687-5265</issn><eissn>1687-5273</eissn><abstract>The 3D tortuosity determined in several brain areas is proposed as a new morphological biomarker (BM) to be considered in early detection of Alzheimer’s disease (AD). It is measured using the sum of angles method and it has proven to be sensitive to anatomical changes that appear in gray and white matter and temporal and parietal lobes during mild cognitive impairment (MCI). Statistical analysis showed significant differences (p&lt;0.05) between tortuosity indices determined for healthy controls (HC) vs. MCI and HC vs. AD in most of the analyzed structures. Other clinically used BMs have also been incorporated in the analysis: beta-amyloid and tau protein CSF and plasma concentrations, as well as other image-extracted parameters. A classification strategy using random forest (RF) algorithms was implemented to discriminate between three samples of the studied populations, selected from the ADNI database. Classification rates considering only image-extracted parameters show an increase of 9.17%, when tortuosity is incorporated. An enhancement of 1.67% is obtained when BMs measured from several modalities are combined with tortuosity.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>32405294</pmid><doi>10.1155/2020/4041832</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6696-7618</orcidid><orcidid>https://orcid.org/0000-0001-8072-3927</orcidid><orcidid>https://orcid.org/0000-0002-4069-4268</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1687-5265
ispartof Computational intelligence and neuroscience, 2020, Vol.2020 (2020), p.1-11
issn 1687-5265
1687-5273
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7204386
source Wiley Online Library Open Access; Publicly Available Content Database
subjects Advertising executives
Aged
Algorithms
Alzheimer Disease - classification
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - pathology
Alzheimer's disease
Artificial intelligence
Automatic classification
Biomarkers
Brain
Brain - diagnostic imaging
Brain - pathology
Brain research
Cerebrospinal fluid
Cognitive ability
Dementia
Diagnosis
Female
Humans
Image classification
Image Interpretation, Computer-Assisted - methods
Machine learning
Magnetic Resonance Imaging
Male
Measurement
Medical imaging
Morphology
Neurodegenerative diseases
Parameters
Pharmaceutical industry
Population studies
Statistical analysis
Statistical methods
Substantia alba
Support vector machines
Tau protein
Tortuosity
β-Amyloid
title Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer’s Disease
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T09%3A12%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluation%20of%20Brain%20Tortuosity%20Measurement%20for%20the%20Automatic%20Multimodal%20Classification%20of%20Subjects%20with%20Alzheimer%E2%80%99s%20Disease&rft.jtitle=Computational%20intelligence%20and%20neuroscience&rft.au=Medina-Ba%C3%B1uelos,%20Ver%C3%B3nica&rft.date=2020&rft.volume=2020&rft.issue=2020&rft.spage=1&rft.epage=11&rft.pages=1-11&rft.issn=1687-5265&rft.eissn=1687-5273&rft_id=info:doi/10.1155/2020/4041832&rft_dat=%3Cgale_pubme%3EA621086040%3C/gale_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c499t-18ec3f948eb2b2a2f8b7b3df2620395be2e2d32d0e6a5c18e4e2e268216bb5f93%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2352596504&rft_id=info:pmid/32405294&rft_galeid=A621086040&rfr_iscdi=true