{"id":"https://openalex.org/W2906830908","doi":"https://doi.org/10.1093/geroni/igy031.3302","title":"MACHINE LEARNING ALGORITHM FOR MEASURING MUSCLE MASS AND MYOSTEOTOSIS USING COMPUTED TOMOGRAPHY","display_name":"MACHINE LEARNING ALGORITHM FOR MEASURING MUSCLE MASS AND MYOSTEOTOSIS USING COMPUTED TOMOGRAPHY","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2906830908","doi":"https://doi.org/10.1093/geroni/igy031.3302","mag":"2906830908"},"language":"en","primary_location":{"id":"pmh:oai:europepmc.org:5214951","doi":"https://doi.org/10.1093/geroni/igy031.3302","is_oa":false,"landing_page_url":"http://europepmc.org/pmc/articles/PMC6239407","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6239407","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039587632","display_name":"Leon Lenchik","orcid":"https://orcid.org/0000-0001-6450-2470","observed_orcids":["https://orcid.org/0000-0001-6450-2470"]},"institutions":[{"id":"https://openalex.org/I4405269449","display_name":"Wake Forest University Health Sciences","ror":"https://ror.org/046r1r105","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]},{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leon Lenchik","raw_affiliation_strings":["Wake Forest School of Medicine"],"raw_orcid":"https://orcid.org/0000-0001-6450-2470","affiliations":[{"raw_affiliation_string":"Wake Forest School of Medicine","institution_ids":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026202191","display_name":"R Lenchik","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405269449","display_name":"Wake Forest University Health Sciences","ror":"https://ror.org/046r1r105","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]},{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R Lenchik","raw_affiliation_strings":["Wake Forest School of Medicine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wake Forest School of Medicine","institution_ids":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089876350","display_name":"Stephen B. Kritchevsky","orcid":"https://orcid.org/0000-0003-3336-6781","observed_orcids":["https://orcid.org/0000-0003-3336-6781"]},"institutions":[{"id":"https://openalex.org/I4405269449","display_name":"Wake Forest University Health Sciences","ror":"https://ror.org/046r1r105","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]},{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stephen B. Kritchevsky","raw_affiliation_strings":["Wake Forest School of Medicine"],"raw_orcid":"https://orcid.org/0000-0003-3336-6781","affiliations":[{"raw_affiliation_string":"Wake Forest School of Medicine","institution_ids":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065718384","display_name":"Ashley A. Weaver","orcid":"https://orcid.org/0000-0002-4383-0106","observed_orcids":["https://orcid.org/0000-0002-4383-0106"]},"institutions":[{"id":"https://openalex.org/I4405269449","display_name":"Wake Forest University Health Sciences","ror":"https://ror.org/046r1r105","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]},{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashley A. Weaver","raw_affiliation_strings":["Wake Forest University School of Medicine"],"raw_orcid":"https://orcid.org/0000-0002-4383-0106","affiliations":[{"raw_affiliation_string":"Wake Forest University School of Medicine","institution_ids":["https://openalex.org/I4405269449","https://openalex.org/I47251452"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029950314","display_name":"Robert Downey Boutin","orcid":"https://orcid.org/0000-0001-7887-9658","observed_orcids":["https://orcid.org/0000-0001-7887-9658"]},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert D. Boutin","raw_affiliation_strings":["UC Davis School of Medicine;"],"raw_orcid":"https://orcid.org/0000-0001-7887-9658","affiliations":[{"raw_affiliation_string":"UC Davis School of Medicine;","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14830752,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"885","last_page":"885"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.8720999956130981,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.8720999956130981,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.030799999833106995,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.02969999983906746,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/muscle-mass-assessment","display_name":"muscle mass assessment","score":0.8180000185966492},{"id":"https://openalex.org/keywords/muscle-segmentation","display_name":"muscle segmentation","score":0.7350000143051147},{"id":"https://openalex.org/keywords/u-net","display_name":"U-Net","score":0.7260000109672546},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9079999923706055},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"computed tomography","score":0.7580000162124634},{"id":"https://openalex.org/keywords/muscle-cross-sectional-area","display_name":"muscle cross-sectional area","score":0.8460000157356262},{"id":"https://openalex.org/keywords/muscle-attenuation","display_name":"muscle attenuation","score":0.9810000061988831},{"id":"https://openalex.org/keywords/national-lung-screening-trial","display_name":"National Lung Screening Trial","score":0.7429999709129333},{"id":"https://openalex.org/keywords/older-adults","display_name":"older adults","score":0.5899999737739563},{"id":"https://openalex.org/keywords/algorithm-validation","display_name":"algorithm validation","score":0.49000000953674316}],"concepts":[{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.6215965151786804},{"id":"https://openalex.org/C2992343062","wikidata":"https://www.wikidata.org/wiki/Q1048687","display_name":"Muscle mass","level":2,"score":0.5743040442466736},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5605329275131226},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5197360515594482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4197880029678345},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.17208415269851685},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.10563388466835022},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.04911303520202637}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:europepmc.org:5214951","doi":"https://doi.org/10.1093/geroni/igy031.3302","is_oa":false,"landing_page_url":"http://europepmc.org/pmc/articles/PMC6239407","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:6239407","doi":"https://doi.org/10.1093/geroni/igy031.3302","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6239407","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Innov Aging","raw_type":"Text"},{"id":"mag:2906830908","doi":"https://doi.org/10.1093/geroni/igy031.3302","is_oa":true,"landing_page_url":"http://europepmc.org/articles/PMC6239407","pdf_url":null,"source":{"id":"https://openalex.org/S2735557189","display_name":"Innovation in Aging","issn_l":"2399-5300","issn":["2399-5300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311647","host_organization_name":"University of Oxford","host_organization_lineage":["https://openalex.org/P4310311647"],"host_organization_lineage_names":["University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Innovation in Aging","raw_type":null}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:6239407","doi":"https://doi.org/10.1093/geroni/igy031.3302","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6239407","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Innov Aging","raw_type":"Text"},"sustainable_development_goals":[{"score":0.9476000070571899,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,133,166],"purpose":[1],"was":[2,51,78,108,168,219],"to":[3,45,97,203],"validate":[4,46],"a":[5,136,174,210],"machine":[6],"learning":[7],"(ML)":[8],"algorithm":[9,104,134,208,235],"for":[10,105,123],"measuring":[11],"muscle":[12,86,92,106,223,229],"mass":[13],"and":[14,69,73,119,158,160,187,191,197,228,236],"myosteotosis":[15],"on":[16],"computed":[17],"tomography":[18],"(CT)":[19],"examinations.":[20],"1404":[21],"non-contrast":[22],"chest":[23],"CT":[24,55],"exams":[25],"from":[26],"older":[27],"participants":[28],"in":[29,59,222],"the":[30,47,54,83,88,120,129,140,163,206],"National":[31],"Lung":[32],"Screening":[33],"Trial":[34],"(mean":[35],"age":[36],"72,":[37],"mean":[38],"BMI":[39],"27.2,":[40],"36%":[41],"female)":[42],"were":[43,200],"used":[44],"algorithm.":[48,165],"This":[49],"dataset":[50],"chosen":[52],"because":[53],"images":[56,149,182],"are":[57],"heterogeneous":[58],"field":[60],"of":[61,128,139,177,215],"view,":[62],"voxel":[63,155],"spacing,":[64],"convolution":[65],"kernel,":[66],"scanner":[67],"manufacturer":[68],"model,":[70],"reconstruction":[71],"algorithm,":[72],"image":[74],"quality.":[75],"Ground":[76],"truth":[77],"obtained":[79],"by":[80],"manually":[81],"segmenting":[82],"left":[84],"paraspinous":[85],"at":[87,95],"T12":[89],"level":[90],"with":[91,147,173,184],"thresholds":[93],"set":[94],"-29":[96],"+150":[98],"Hounsfield":[99],"units":[100],"(HU).":[101],"Our":[102],"ML":[103,196,207,234],"segmentation":[107,199],"developed":[109],"using":[110,162,179],"open-source":[111],"tools:":[112],"Keras,":[113],"TensorFlow,":[114],"Pandas,":[115],"NumPy,":[116],"scikit-image,":[117],"Snakemake,":[118],"Oxford":[121],"Centre":[122],"Functional":[124],"Magnetic":[125],"Resonance":[126],"Imaging":[127],"Brain":[130],"Software":[131],"Library.":[132],"is":[135],"Keras-based":[137],"implementation":[138],"U-Net":[141],"convolutional":[142],"neural":[143],"network":[144],"(CNN)":[145],"architecture,":[146],"input":[148],"pre-processed":[150],"into":[151],"three":[152],"channels:":[153],"raw":[154],"values,":[156],"clamped":[157],"rescaled,":[159],"equalized":[161],"CLAHE":[164],"model":[167],"trained":[169],"over":[170],"100":[171],"epochs":[172],"batch":[175],"size":[176],"30,":[178],"690":[180],"training":[181],"augmented":[183],"rotation,":[185],"translation,":[186],"scaling.":[188],"Pearson":[189],"correlation":[190,221],"Dice":[192,212],"similarity":[193,213],"coefficients":[194],"between":[195,233],"manual":[198,204],"determined.":[201],"Compared":[202],"segmentation,":[205],"had":[209],"median":[211],"coefficient":[214],"0.91":[216],"(SD=0.06).":[217],"There":[218],"high":[220],"cross-sectional":[224],"area":[225],"(r=0.89;":[226],"p<0.0001)":[227,232],"attenuation":[230],"(r=0.92;":[231],"ground":[237],"truth.":[238]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2025-10-10T00:00:00"}