{"id":"https://openalex.org/W7170045593","doi":"https://doi.org/10.48550/arxiv.2607.18638","title":"Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs","display_name":"Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs","publication_year":2026,"publication_date":"2026-07-21","ids":{"openalex":"https://openalex.org/W7170045593","doi":"https://doi.org/10.48550/arxiv.2607.18638"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.18638","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18638","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.18638","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020726867","display_name":"Tomohiro Kikuchi","orcid":"https://orcid.org/0000-0002-4222-4569"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kikuchi, Tomohiro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102501626","display_name":"Kohei Yamamoto","orcid":"https://orcid.org/0000-0002-1733-458X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yamamoto, Kohei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072295276","display_name":"Yukihiro Nomura","orcid":"https://orcid.org/0000-0001-6471-9936"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nomura, Yukihiro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019191233","display_name":"Yosuke Yamagishi","orcid":"https://orcid.org/0009-0006-7688-3075"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yamagishi, Yosuke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143422781","display_name":"Takeharu Yoshikawa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yoshikawa, Takeharu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060878563","display_name":"Toshiaki Akashi","orcid":"https://orcid.org/0000-0002-3056-0792"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akashi, Toshiaki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032934383","display_name":"Jun Kamohara","orcid":"https://orcid.org/0000-0002-0158-383X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kamohara, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058851367","display_name":"Hiroyuki Fujii","orcid":"https://orcid.org/0000-0002-7528-7654"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fujii, Hiroyuki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5062029974","display_name":"Harushi Mori","orcid":"https://orcid.org/0000-0003-1370-5272"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mori, Harushi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10992","display_name":"Forensic Anthropology and Bioarchaeology Studies","score":0.5523999929428101,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10992","display_name":"Forensic Anthropology and Bioarchaeology Studies","score":0.5523999929428101,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12296","display_name":"Autopsy Techniques and Outcomes","score":0.11680000275373459,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.02319999970495701,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/coronal-plane","display_name":"Coronal plane","score":0.7784000039100647},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.7548999786376953},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.6183000206947327},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5293999910354614},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.438400000333786},{"id":"https://openalex.org/keywords/body-weight","display_name":"Body weight","score":0.40230000019073486},{"id":"https://openalex.org/keywords/weight-estimation","display_name":"Weight estimation","score":0.39649999141693115},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.37070000171661377}],"concepts":[{"id":"https://openalex.org/C13483470","wikidata":"https://www.wikidata.org/wiki/Q1470152","display_name":"Coronal plane","level":2,"score":0.7784000039100647},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.7548999786376953},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.6183000206947327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5874999761581421},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5293999910354614},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.46939998865127563},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.438400000333786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4242999851703644},{"id":"https://openalex.org/C147583825","wikidata":"https://www.wikidata.org/wiki/Q620876","display_name":"Body weight","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39969998598098755},{"id":"https://openalex.org/C3019530228","wikidata":"https://www.wikidata.org/wiki/Q620876","display_name":"Weight estimation","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.38429999351501465},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C29694066","wikidata":"https://www.wikidata.org/wiki/Q118301","display_name":"Orthodontics","level":1,"score":0.31779998540878296},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.311599999666214},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30720001459121704},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C2777501473","wikidata":"https://www.wikidata.org/wiki/Q1796666","display_name":"Body surface area","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.18638","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18638","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.18638","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18638","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6398683786392212}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Purpose:":[0],"To":[1],"develop":[2],"and":[3,15,28,49,55,75,89,100,103,112,123,147,156,163,176,183,195,223],"validate":[4],"a":[5],"deep":[6],"learning":[7],"ensemble":[8,217],"for":[9,160],"estimating":[10],"adult":[11,219],"sex,":[12,220],"age,":[13,101,131,161,221],"height,":[14,102,162,222],"weight":[16,104,224],"from":[17,24,37,189,225],"coronal":[18,53],"digitally":[19],"reconstructed":[20],"radiographs":[21],"(DRRs)":[22],"generated":[23],"diagnostic":[25],"CT.":[26],"Materials":[27],"Methods:":[29],"This":[30],"retrospective":[31],"study":[32],"included":[33],"128,621":[34],"CT":[35],"examinations":[36,167,233],"80,004":[38],"adults":[39],"at":[40],"nine":[41],"institutions":[42],"in":[43,232],"Japan.":[44],"Three":[45],"multitask":[46],"models-ConvNeXt-Base,":[47],"ViT-Base/16,":[48],"MaxViT-Base-were":[50],"fine-tuned":[51],"using":[52,118,200],"DRRs":[54],"combined":[56],"by":[57,63,212],"weighted":[58],"averaging.":[59],"Data":[60],"were":[61,94,116,149,178],"split":[62],"institution":[64],"into":[65],"training":[66],"(114,147":[67],"examinations;":[68],"seven":[69],"institutions),":[70],"tuning":[71],"(4,305;":[72],"one":[73,78],"institution),":[74],"test":[76,128],"(10,169;":[77],"institution)":[79],"sets;":[80],"generalizability":[81],"was":[82,142,174,210],"assessed":[83],"on":[84],"two":[85],"non-Japanese":[86,204],"datasets.":[87],"Accuracy":[88],"mean":[90],"absolute":[91],"error":[92,207],"(MAE)":[93],"used":[95],"to":[96],"evaluate":[97],"sex":[98],"classification":[99],"regression,":[105],"respectively.":[106,165,186],"Body":[107],"surface":[108],"area":[109],"(BSA)-corrected":[110],"heart":[111,194],"liver":[113,196],"volume":[114,197],"trends":[115,198],"compared":[117],"true":[119,201],"versus":[120],"estimated":[121,190,218],"height":[122,206],"weight.":[124],"Results:":[125],"In":[126,166],"the":[127,169],"set":[129],"(median":[130],"69.9":[132],"years;":[133],"4,899":[134],"of":[135],"10,169":[136],"[48.2%]":[137],"male),":[138],"overall":[139],"sex-classification":[140],"accuracy":[141,173],"0.997":[143],"(95%":[144],"CI,":[145],"0.996-0.998),":[146],"MAEs":[148,177],"3.57":[150],"years":[151],"(3.51-3.63),":[152],"2.59":[153],"cm":[154],"(2.54-2.64),":[155],"3.40":[157],"kg":[158],"(3.34-3.47)":[159],"weight,":[164],"covering":[168],"chest":[170],"through":[171],"pelvis,":[172],"1.000,":[175],"3.15":[179],"years,":[180],"2.28":[181],"cm,":[182],"3.18":[184],"kg,":[185],"BSA":[187],"calculated":[188],"values":[191],"reproduced":[192],"age-related":[193],"obtained":[199],"values.":[202],"On":[203],"datasets,":[205],"increased":[208],"but":[209],"reduced":[211],"continued":[213],"fine-tuning.":[214],"Conclusion:":[215],"The":[216],"CT-derived":[226],"DRRs,":[227],"with":[228,234],"generally":[229],"lower":[230],"errors":[231],"broader":[235],"anatomical":[236],"coverage.":[237]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-23T00:00:00"}