{"id":"https://openalex.org/W7162292641","doi":"https://doi.org/10.48550/arxiv.2605.23653","title":"ExpOS: Explainable Open-Surgery Skills Assessment Using 3D Hand Reconstruction","display_name":"ExpOS: Explainable Open-Surgery Skills Assessment Using 3D Hand Reconstruction","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162292641","doi":"https://doi.org/10.48550/arxiv.2605.23653"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23653","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.23653","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5096115335","display_name":"Roi Papo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Papo, Roi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136979787","display_name":"Idan Smoller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smoller, Idan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5035442169","display_name":"Shlomi Laufer","orcid":"https://orcid.org/0000-0003-1469-5985"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laufer, Shlomi","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/T10916","display_name":"Surgical Simulation and Training","score":0.9747999906539917,"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/T10916","display_name":"Surgical Simulation and Training","score":0.9747999906539917,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.003700000001117587,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12290","display_name":"Human Motion and Animation","score":0.003100000089034438,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7487000226974487},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.6452000141143799},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6175000071525574},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.541700005531311},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4837000072002411},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.44029998779296875},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.4302000105381012},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4066999852657318}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7487000226974487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6697999835014343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6514999866485596},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.6452000141143799},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6175000071525574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5631999969482422},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.541700005531311},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.44029998779296875},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.4302000105381012},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4066999852657318},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3127000033855438},{"id":"https://openalex.org/C2777036941","wikidata":"https://www.wikidata.org/wiki/Q6917771","display_name":"Motion analysis","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23653","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.23653","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23653","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6450504064559937,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Timely":[0],"and":[1,20,58,62,71,87,99,133,151,199],"transparent":[2],"feedback":[3],"is":[4],"essential":[5],"for":[6,22,31],"effective":[7],"surgical":[8,201],"training,":[9],"yet":[10],"current":[11],"assessment":[12,33],"remains":[13],"dependent":[14],"on":[15,46,75,175],"expert":[16,170],"observation,":[17],"limiting":[18],"scalability":[19],"opportunities":[21],"autonomous":[23],"practice.":[24],"We":[25,69],"present":[26],"ExpOS,":[27],"an":[28],"explainable":[29],"framework":[30,165],"data-driven":[32],"of":[34,66,78],"open-surgery":[35,83],"skills":[36],"designed":[37],"to":[38,95,116,129,134],"enable":[39],"automatic,":[40],"feedback-oriented":[41],"evaluation.":[42],"Rather":[43],"than":[44],"relying":[45],"expert-defined":[47],"metrics,":[48],"ExpOS":[49,138],"learns":[50],"discriminative":[51],"temporal":[52,110,190],"patterns":[53],"directly":[54],"from":[55,92],"motion":[56,101,127,153,195],"data":[57],"identifies":[59],"the":[60,73,164],"segments":[61],"behaviors":[63],"most":[64,155],"predictive":[65],"skill":[67,131,202],"level.":[68],"trained":[70],"evaluated":[72],"method":[74],"221":[76],"videos":[77],"medical":[79],"students":[80],"performing":[81],"three":[82],"tasks.":[84],"Hand":[85],"poses":[86],"tool":[88],"detections":[89],"were":[90,106,123],"extracted":[91],"each":[93],"frame":[94],"derive":[96],"kinematic":[97],"descriptors":[98],"global":[100,126,159],"statistics.":[102],"Spatiotemporal":[103],"hand-tool":[104],"dynamics":[105],"modeled":[107],"using":[108],"a":[109],"convolutional":[111],"backbone":[112],"with":[113,125,169,172,193],"attention-based":[114],"pooling":[115],"generate":[117],"frame-level":[118],"importance":[119,191],"maps.":[120],"These":[121,184],"representations":[122],"fused":[124],"statistics":[128,196],"predict":[130],"level":[132],"provide":[135],"interpretable":[136,194],"feedback.":[137],"provides":[139],"multi-level":[140],"explainability":[141],"by":[142],"identifying":[143],"when":[144],"informative":[145],"events":[146],"occur":[147],"through":[148,158],"attention":[149],"weights":[150],"which":[152],"characteristics":[154],"influence":[156],"predictions":[157],"feature":[160],"analysis.":[161],"Across":[162],"tasks,":[163],"achieved":[166],"strong":[167],"correlation":[168],"ratings,":[171],"best":[173],"performance":[174],"fascial":[176],"closure":[177],"(r":[178],"=":[179,182],"0.778,":[180],"R2":[181],"0.74).":[183],"results":[185],"demonstrate":[186],"that":[187],"combining":[188],"weakly-supervised":[189],"learning":[192],"enables":[197],"scalable":[198],"actionable":[200],"assessment.":[203]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-26T00:00:00"}