{"id":"https://openalex.org/W7155187585","doi":"https://doi.org/10.48550/arxiv.2604.18740","title":"Autonomous Skeletal Landmark Localization towards Agentic C-Arm Control","display_name":"Autonomous Skeletal Landmark Localization towards Agentic C-Arm Control","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155187585","doi":"https://doi.org/10.48550/arxiv.2604.18740"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18740","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.2604.18740","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134221010","display_name":"Jay Jung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jung, Jay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092094033","display_name":"Ahmad Arrabi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arrabi, Ahmad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125739650","display_name":"Jax Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Jax","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076473916","display_name":"S. Raymond","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raymond, Scott","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5001816279","display_name":"Safwan Wshah","orcid":"https://orcid.org/0000-0001-5051-7719"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wshah, Safwan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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/T10510","display_name":"Stroke Rehabilitation and Recovery","score":0.09700000286102295,"subfield":{"id":"https://openalex.org/subfields/2742","display_name":"Rehabilitation"},"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/T10510","display_name":"Stroke Rehabilitation and Recovery","score":0.09700000286102295,"subfield":{"id":"https://openalex.org/subfields/2742","display_name":"Rehabilitation"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.08829999715089798,"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"}},{"id":"https://openalex.org/T10562","display_name":"Total Knee Arthroplasty Outcomes","score":0.07989999651908875,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.9710000157356262},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.5627999901771545},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3521000146865845},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.32989999651908875},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.27639999985694885}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.9710000157356262},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6916000247001648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6306999921798706},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.5627999901771545},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3637000024318695},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3521000146865845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33709999918937683},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2639999985694885}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18740","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.2604.18740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18740","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Purpose:":[0],"Automated":[1],"C-arm":[2,20,36,71,162,217],"positioning":[3],"ensures":[4],"timely":[5],"treatment":[6],"in":[7,30,97],"patients":[8],"requiring":[9],"emergent":[10],"interventions.":[11],"When":[12],"a":[13,91,131,164],"conventional":[14],"Deep":[15],"Learning":[16],"(DL)":[17],"approach":[18],"for":[19,70,78,214],"control":[21,37],"fails,":[22],"clinicians":[23],"must":[24],"revert":[25],"to":[26,58],"manual":[27],"operation,":[28],"resulting":[29],"additional":[31],"delays.":[32],"Consequently,":[33],"an":[34,85,143,147],"agentic":[35,215],"framework":[38],"based":[39],"on":[40],"multimodal":[41],"large":[42],"language":[43],"models":[44],"(MLLMs)":[45],"is":[46,68,100,221],"highly":[47],"desirable,":[48],"as":[49],"it":[50],"can":[51,145,158],"incorporate":[52],"clinician":[53],"feedback":[54],"and":[55,73,90,110,128,153,196,211],"use":[56],"reasoning":[57,195],"make":[59],"adjustments":[60],"toward":[61,163],"more":[62],"accurate":[63,207],"positioning.":[64],"Skeletal":[65],"landmark":[66,80,124,209],"localization":[67,125,178,210],"essential":[69],"control,":[72],"we":[74],"investigate":[75],"adapting":[76],"MLLMs":[77,109,172,191,205],"autonomous":[79,216],"localization.":[81],"Methods:":[82],"We":[83,106,135],"used":[84],"annotated":[86],"synthetic":[87],"X-ray":[88,93,96],"dataset":[89],"real":[92],"dataset.":[94],"Each":[95],"both":[98,169],"datasets":[99],"paired":[101],"with":[102,113,182],"several":[103],"skeletal":[104,208],"landmarks.":[105],"fine-tuned":[107,171,204],"two":[108],"tasked":[111],"them":[112],"retrieving":[114],"the":[115,156,161,183,187,190],"closest":[116],"landmarks":[117],"from":[118],"each":[119],"X-ray.":[120],"Quantitative":[121],"evaluations":[122],"of":[123,194],"were":[126],"performed":[127],"compared":[129,181],"against":[130],"leading":[132],"DL":[133,184],"approach.":[134,185],"further":[136],"conducted":[137],"qualitative":[138,188],"experiments":[139],"demonstrating:":[140],"(1)":[141],"how":[142,155],"MLLM":[144,157],"correct":[146],"initially":[148],"incorrect":[149],"prediction":[150],"through":[151],"reasoning,":[152],"(2)":[154],"sequentially":[159],"navigate":[160],"target":[165],"location.":[166],"Results:":[167],"On":[168],"datasets,":[170],"demonstrate":[173],"competitive":[174],"performance":[175],"across":[176],"all":[177],"tasks":[179],"when":[180],"In":[186],"experiments,":[189],"provide":[192],"evidence":[193],"spatial":[197],"awareness.":[198],"Conclusion:":[199],"This":[200],"study":[201],"shows":[202],"that":[203],"achieve":[206],"hold":[212],"promise":[213],"control.":[218],"Our":[219],"code":[220],"available":[222],"athttps://github.com/marszzibros/C-arm-localization-LLMs.git":[223]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-23T00:00:00"}