{"id":"https://openalex.org/W2919487645","doi":"https://doi.org/10.1109/jbhi.2019.2903190","title":"3D Shape-Based Body Composition Inference Model Using a Bayesian Network","display_name":"3D Shape-Based Body Composition Inference Model Using a Bayesian Network","publication_year":2019,"publication_date":"2019-03-05","ids":{"openalex":"https://openalex.org/W2919487645","doi":"https://doi.org/10.1109/jbhi.2019.2903190","mag":"2919487645","pmid":"https://pubmed.ncbi.nlm.nih.gov/30843854"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2019.2903190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2019.2903190","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101490957","display_name":"Yao Lu","orcid":"https://orcid.org/0000-0001-7680-1531"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Lu","raw_affiliation_strings":["Department of Computer Science, George Washington University, Washington, USA"],"raw_orcid":"https://orcid.org/0000-0001-7680-1531","affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Washington University, Washington, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009074303","display_name":"James K. Hahn","orcid":"https://orcid.org/0000-0001-6535-8175"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James K Hahn","raw_affiliation_strings":["Department of Computer Science, George Washington University, Washington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Washington University, Washington, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100653626","display_name":"Xiaoke Zhang","orcid":"https://orcid.org/0000-0002-7272-1523"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoke Zhang","raw_affiliation_strings":["Department of Statistics, George Washington University, Washington, USA"],"raw_orcid":"https://orcid.org/0000-0002-7272-1523","affiliations":[{"raw_affiliation_string":"Department of Statistics, George Washington University, Washington, USA","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":1.1636,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.78273454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"24","issue":"1","first_page":"205","last_page":"213"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12279","display_name":"Body Composition Measurement Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12279","display_name":"Body Composition Measurement Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11109","display_name":"Thermoregulation and physiological responses","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9922000169754028,"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/computer-science","display_name":"Computer science","score":0.6414271593093872},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6068507432937622},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5732200145721436},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44338953495025635},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4157698452472687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3762282431125641},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33517932891845703},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.256197988986969},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24406415224075317}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6414271593093872},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6068507432937622},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5732200145721436},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44338953495025635},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4157698452472687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3762282431125641},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33517932891845703},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.256197988986969},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24406415224075317}],"mesh":[{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001823","descriptor_name":"Body Composition","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D001823","descriptor_name":"Body Composition","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D001823","descriptor_name":"Body Composition","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D051598","descriptor_name":"Whole Body Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D051598","descriptor_name":"Whole Body Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D051598","descriptor_name":"Whole Body Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057225","descriptor_name":"Data Mining","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1109/jbhi.2019.2903190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2019.2903190","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:30843854","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30843854","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE journal of biomedical and health informatics","raw_type":null},{"id":"pmh:oai:europepmc.org:5901004","is_oa":false,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6728241","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,"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":null,"raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1424343359","display_name":null,"funder_award_id":"DMS-1832046","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1898974627","display_name":null,"funder_award_id":"R01 HD091179","funder_id":"https://openalex.org/F4320337611","funder_display_name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development"},{"id":"https://openalex.org/G3398267958","display_name":null,"funder_award_id":"CNS-1337722","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5519499961","display_name":null,"funder_award_id":"R01HD091179","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6150169675","display_name":null,"funder_award_id":"R21 HL124443","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"},{"id":"https://openalex.org/G7284206903","display_name":null,"funder_award_id":"R21HL124443","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337338","display_name":"National Heart, Lung, and Blood Institute","ror":"https://ror.org/012pb6c26"},{"id":"https://openalex.org/F4320337611","display_name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","ror":"https://ror.org/04byxyr05"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W136440157","https://openalex.org/W194145145","https://openalex.org/W1511986666","https://openalex.org/W1572520031","https://openalex.org/W1950934168","https://openalex.org/W1972380049","https://openalex.org/W1990672387","https://openalex.org/W1998778007","https://openalex.org/W2000111445","https://openalex.org/W2003890675","https://openalex.org/W2020236439","https://openalex.org/W2021830033","https://openalex.org/W2044952536","https://openalex.org/W2084123965","https://openalex.org/W2088365881","https://openalex.org/W2099502056","https://openalex.org/W2105023507","https://openalex.org/W2123371309","https://openalex.org/W2123977609","https://openalex.org/W2125930781","https://openalex.org/W2129412583","https://openalex.org/W2135301926","https://openalex.org/W2135523822","https://openalex.org/W2163614729","https://openalex.org/W2168745915","https://openalex.org/W2470421573","https://openalex.org/W2476586481","https://openalex.org/W2748329920","https://openalex.org/W2760191199","https://openalex.org/W2802902066","https://openalex.org/W2899400049","https://openalex.org/W2920981733","https://openalex.org/W2942879726","https://openalex.org/W4229977739","https://openalex.org/W4293116348","https://openalex.org/W6607887928","https://openalex.org/W6683915439","https://openalex.org/W6762832499"],"related_works":["https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2047973478","https://openalex.org/W2067569035","https://openalex.org/W3135697610","https://openalex.org/W2090985514","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319"],"abstract_inverted_index":{"Body":[0],"composition":[1,89,111,185],"can":[2,55],"be":[3],"assessed":[4],"in":[5,33,171,180,208],"many":[6],"different":[7],"ways.":[8],"High-end":[9],"medical":[10],"equipment,":[11],"such":[12,77],"as":[13,78,167],"Dual-energy":[14],"X-ray":[15],"Absorptiometry":[16],"(DXA),":[17],"Computed":[18],"Tomography":[19],"(CT),":[20],"and":[21,40,74,112,121,174],"Magnetic":[22],"Resonance":[23],"Imaging":[24],"(MRI)":[25],"offers":[26],"high-fidelity":[27],"pixel/voxel-level":[28],"assessment,":[29,186,214],"but":[30,60],"is":[31,63],"prohibitive":[32],"cost.":[34],"In":[35,97],"the":[36,42,61,66,144,147,158,168,188,216,223],"case":[37],"of":[38,114,146,182,210],"DXA":[39,165],"CT,":[41],"approach":[43],"exposes":[44],"users":[45],"to":[46,65,107,129,142],"ionizing":[47],"radiation.":[48],"Whole-body":[49],"air":[50],"displacement":[51],"plethysmography":[52],"(BOD":[53],"POD)":[54],"accurately":[56],"estimate":[57],"body":[58,88,94,110,115,122,127,184],"density,":[59],"assessment":[62,90,166],"limited":[64],"whole-body":[67,211],"fat":[68,116,132,212],"percentage.":[69],"Optical":[70],"three-dimensional":[71],"(3D)":[72],"scan":[73],"reconstruction":[75],"techniques,":[76],"using":[79,117,160],"depth":[80],"cameras,":[81],"have":[82],"brought":[83],"new":[84],"opportunities":[85],"for":[86],"improving":[87],"by":[91,199,226],"intelligently":[92],"analyzing":[93],"shape":[95],"features.":[96,154],"this":[98],"paper,":[99],"we":[100,125,137,156],"present":[101],"a":[102,131,139],"novel":[103],"supervised":[104],"inference":[105],"model":[106,130,172],"predict":[108],"pixel-level":[109,183],"percentage":[113,213],"3D":[118,152],"geometry":[119,153],"features":[120],"density.":[123],"First,":[124],"use":[126,138,164],"density":[128],"distribution":[133],"base":[134,148],"prediction.":[135],"Then,":[136],"Bayesian":[140],"network":[141],"infer":[143],"probability":[145],"prediction":[149,191,197],"bias":[150,159],"with":[151,187,215],"Finally,":[155],"correct":[157],"non-parametric":[161],"regression.":[162],"We":[163,176,203],"ground":[169],"truth":[170],"training":[173],"validation.":[175],"compare":[177,205],"our":[178,206],"method,":[179,207],"terms":[181,209],"current":[189],"state-of-the-art":[190],"models.":[192],"Our":[193,220],"method":[194,221],"outperforms":[195,222],"those":[196],"models":[198],"52.69%":[200],"on":[201],"average.":[202],"also":[204],"medical-level":[217],"equipment-BOD":[218],"POD.":[219],"BOD":[224],"POD":[225],"23.28%.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
