{"id":"https://openalex.org/W7155404709","doi":"https://doi.org/10.48550/arxiv.2604.20268","title":"Opportunistic Bone-Loss Screening from Routine Knee Radiographs Using a Multi-Task Deep Learning Framework with Sensitivity-Constrained Threshold Optimization","display_name":"Opportunistic Bone-Loss Screening from Routine Knee Radiographs Using a Multi-Task Deep Learning Framework with Sensitivity-Constrained Threshold Optimization","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155404709","doi":"https://doi.org/10.48550/arxiv.2604.20268"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20268","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20268","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.20268","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044564280","display_name":"Zhen Li","orcid":"https://orcid.org/0009-0008-1247-0284"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhaochen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103160873","display_name":"Xinghao Yan","orcid":"https://orcid.org/0000-0001-9262-1426"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Xinghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124009136","display_name":"Runni Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Runni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134369604","display_name":"Xiaoyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134446114","display_name":"Chenjie Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Chenjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134366486","display_name":"Gege Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Gege","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101897165","display_name":"Yu Shi","orcid":"https://orcid.org/0000-0001-8225-8678"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134403582","display_name":"Lixin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134457852","display_name":"Rongrong Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Rongrong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134420129","display_name":"Liehao Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Liehao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130090887","display_name":"Yuan Chai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chai, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":11,"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/T10562","display_name":"Total Knee Arthroplasty Outcomes","score":0.4169999957084656,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T10562","display_name":"Total Knee Arthroplasty Outcomes","score":0.4169999957084656,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T10105","display_name":"Osteoarthritis Treatment and Mechanisms","score":0.21119999885559082,"subfield":{"id":"https://openalex.org/subfields/2745","display_name":"Rheumatology"},"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/T10071","display_name":"Bone health and osteoporosis research","score":0.10859999805688858,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"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/radiography","display_name":"Radiography","score":0.6080999970436096},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5830000042915344},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5730000138282776},{"id":"https://openalex.org/keywords/osteoporosis","display_name":"Osteoporosis","score":0.42660000920295715},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.41760000586509705},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.38089999556541443},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.3763999938964844},{"id":"https://openalex.org/keywords/osteopenia","display_name":"Osteopenia","score":0.36239999532699585},{"id":"https://openalex.org/keywords/intraclass-correlation","display_name":"Intraclass correlation","score":0.361299991607666}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.630299985408783},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.6080999970436096},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5830000042915344},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5730000138282776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5600000023841858},{"id":"https://openalex.org/C2776541429","wikidata":"https://www.wikidata.org/wiki/Q165328","display_name":"Osteoporosis","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.3763999938964844},{"id":"https://openalex.org/C2777083390","wikidata":"https://www.wikidata.org/wiki/Q1367706","display_name":"Osteopenia","level":4,"score":0.36239999532699585},{"id":"https://openalex.org/C104709138","wikidata":"https://www.wikidata.org/wiki/Q1671540","display_name":"Intraclass correlation","level":3,"score":0.361299991607666},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.35120001435279846},{"id":"https://openalex.org/C1862650","wikidata":"https://www.wikidata.org/wiki/Q186005","display_name":"Physical therapy","level":1,"score":0.3425000011920929},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.3264999985694885},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.32420000433921814},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.301800012588501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30070000886917114},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C2776164576","wikidata":"https://www.wikidata.org/wiki/Q62736","display_name":"Osteoarthritis","level":3,"score":0.2838999927043915},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.28349998593330383},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20268","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20268","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20268","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20268","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Background:":[0],"Osteoporosis":[1],"and":[2,39,51,98,120,156,178,214,226],"osteopenia":[3],"are":[4,31],"often":[5],"undiagnosed":[6],"until":[7],"fragility":[8],"fractures":[9],"occur.":[10],"Dual-energy":[11],"X-ray":[12],"absorptiometry":[13],"(DXA)":[14],"is":[15,237],"the":[16,148,162],"reference":[17],"standard":[18],"for":[19,36,44,58,79,182],"bone":[20],"mineral":[21],"density":[22],"(BMD)":[23],"assessment,":[24],"but":[25],"access":[26],"remains":[27],"limited.":[28],"Knee":[29],"radiographs":[30,65],"obtained":[32],"at":[33,147],"high":[34],"volume":[35],"osteoarthritis":[37],"evaluation":[38],"may":[40],"offer":[41],"an":[42,168,188],"opportunity":[43],"opportunistic":[45,59],"bone-loss":[46,60,222],"screening.":[47,184],"Objective:":[48],"To":[49],"develop":[50],"evaluate":[52],"a":[53,76,87,95,99,197,206],"multi-task":[54,77],"deep":[55],"learning":[56],"system":[57],"screening":[61,110],"from":[62,230],"routine":[63,231],"knee":[64,82,144,232],"without":[66],"additional":[67],"imaging":[68],"or":[69],"patient":[70,149],"visits.":[71],"Methods:":[72],"We":[73],"developed":[74],"STR-Net,":[75],"framework":[78],"single-channel":[80],"grayscale":[81],"radiographs.":[83,233],"The":[84,140,192],"model":[85],"includes":[86],"shared":[88,96],"backbone,":[89],"global":[90],"average":[91],"pooling":[92],"feature":[93],"aggregation,":[94],"neck,":[97],"task-aware":[100],"representation":[101],"routing":[102],"module":[103],"connected":[104],"to":[105],"three":[106],"task-specific":[107],"heads:":[108],"binary":[109,183],"(Normal":[111],"vs.":[112,118],"Bone":[113],"Loss),":[114],"severity":[115,224],"sub-classification":[116,186],"(Osteopenia":[117],"Osteoporosis),":[119],"weakly":[121],"coupled":[122],"T-score":[123,193,228],"regression":[124,194],"with":[125,202,210],"optional":[126],"clinical":[127,235],"variables.":[128],"A":[129],"sensitivity-constrained":[130],"threshold":[131],"optimization":[132],"strategy":[133],"(minimum":[134],"sensitivity":[135,172],">=":[136],"0.86)":[137],"was":[138],"applied.":[139],"dataset":[141],"included":[142],"1,570":[143],"radiographs,":[145],"split":[146],"level":[150],"into":[151],"training":[152],"(n=1,120),":[153],"validation":[154,236],"(n=226),":[155],"test":[157,164],"(n=224)":[158],"sets.":[159],"Results:":[160],"On":[161],"held-out":[163],"set,":[165],"STR-Net":[166,219],"achieved":[167,187],"AUROC":[169,189],"of":[170,173,176,180,190,200,212,216],"0.933,":[171],"0.904,":[174],"specificity":[175],"0.773,":[177],"AUPRC":[179],"0.956":[181],"Severity":[185],"0.898.":[191],"branch":[195],"showed":[196],"Pearson":[198],"correlation":[199],"0.801":[201],"DXA-measured":[203],"T-scores":[204],"in":[205],"pilot":[207],"subset":[208],"(n=31),":[209],"MAE":[211],"0.279":[213],"RMSE":[215],"0.347.":[217],"Conclusions:":[218],"enables":[220],"single-pass":[221],"screening,":[223],"stratification,":[225],"quantitative":[227],"estimation":[229],"Prospective":[234],"needed":[238],"before":[239],"deployment.":[240]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-24T00:00:00"}