{"id":"https://openalex.org/W4393568812","doi":"https://doi.org/10.1117/12.3006053","title":"The pancreas is all you need: fusion models for opportunistic screening of diabetes using pancreatic CT volumes","display_name":"The pancreas is all you need: fusion models for opportunistic screening of diabetes using pancreatic CT volumes","publication_year":2024,"publication_date":"2024-04-02","ids":{"openalex":"https://openalex.org/W4393568812","doi":"https://doi.org/10.1117/12.3006053"},"language":"en","primary_location":{"id":"doi:10.1117/12.3006053","doi":"https://doi.org/10.1117/12.3006053","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.3006053","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"SPIE Medical Imaging: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2024: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"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/A5001996891","display_name":"Abhinav Suri","orcid":"https://orcid.org/0000-0002-7900-8763","observed_orcids":["https://orcid.org/0000-0002-7900-8763"]},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Suri","raw_affiliation_strings":["National Institutes of Health (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institutes of Health (United States)","institution_ids":["https://openalex.org/I1299303238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075266423","display_name":"Pritam Mukherjee","orcid":"https://orcid.org/0000-0002-9975-9994","observed_orcids":["https://orcid.org/0000-0002-9975-9994","https://orcid.org/0009-0005-6516-7797","https://orcid.org/0009-0007-9018-4083"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pritam Mukherjee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016047550","display_name":"Ronald M. Summers","orcid":"https://orcid.org/0000-0001-8081-7376","observed_orcids":["https://orcid.org/0000-0001-8081-7376"]},"institutions":[{"id":"https://openalex.org/I1299303238","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ronald M. Summers","raw_affiliation_strings":["National Institutes of Health (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institutes of Health (United States)","institution_ids":["https://openalex.org/I1299303238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02895093,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"47","last_page":"47"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.6708999872207642,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.6708999872207642,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.07419999688863754,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.050700001418590546,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/diabetes-screening","display_name":"diabetes screening","score":0.6819999814033508},{"id":"https://openalex.org/keywords/opportunistic-screening","display_name":"opportunistic screening","score":0.8709999918937683},{"id":"https://openalex.org/keywords/abdominal-ct","display_name":"abdominal CT","score":0.7269999980926514},{"id":"https://openalex.org/keywords/pancreas-segmentation","display_name":"pancreas segmentation","score":0.8700000047683716},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.5820000171661377},{"id":"https://openalex.org/keywords/diabetes-prediction","display_name":"diabetes prediction","score":0.6060000061988831},{"id":"https://openalex.org/keywords/undiagnosed-diabetes","display_name":"undiagnosed diabetes","score":0.8299999833106995},{"id":"https://openalex.org/keywords/adiposity-markers","display_name":"adiposity markers","score":0.578000009059906}],"concepts":[{"id":"https://openalex.org/C2778764654","wikidata":"https://www.wikidata.org/wiki/Q9618","display_name":"Pancreas","level":2,"score":0.6124721765518188},{"id":"https://openalex.org/C555293320","wikidata":"https://www.wikidata.org/wiki/Q12206","display_name":"Diabetes mellitus","level":2,"score":0.5705516934394836},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5345712304115295},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4878978431224823},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4775175154209137},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.32364413142204285},{"id":"https://openalex.org/C134018914","wikidata":"https://www.wikidata.org/wiki/Q162606","display_name":"Endocrinology","level":1,"score":0.09905850887298584},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.3006053","doi":"https://doi.org/10.1117/12.3006053","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.3006053","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"SPIE Medical Imaging: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2024: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.9940000176429749,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4416454329","https://openalex.org/W4225813841","https://openalex.org/W4416730619","https://openalex.org/W4408741613","https://openalex.org/W7167834887","https://openalex.org/W4401249337","https://openalex.org/W3090426973","https://openalex.org/W7214928143","https://openalex.org/W4410430919","https://openalex.org/W4386295956"],"abstract_inverted_index":{"Diabetes":[0],"is":[1,93],"a":[2,18,22,100,114],"disease":[3,174],"that":[4,75,121],"affects":[5],"hundreds":[6],"of":[7,9,26,39,68,77,82,116,129,131,150,161,170],"millions":[8,25],"people":[10,14,27],"worldwide.":[11],"While":[12],"many":[13],"are":[15,79],"diagnosed":[16],"via":[17],"blood":[19],"test":[20],"during":[21],"routine":[23],"visit,":[24],"go":[28],"undiagnosed":[29,58],"due":[30],"to":[31,33,45,55,87,95,103,125],"inability":[32],"access":[34],"primary":[35],"care.":[36],"Opportunistic":[37],"screening":[38],"CT":[40,71,110],"imaging":[41,64,165],"offers":[42],"an":[43,53],"opportunity":[44,157],"predict":[46,104,141],"and":[47,105,138,173],"diagnose":[48,96,106],"diabetes":[49,69,107,142,171],"on":[50],"imaging,":[51],"opening":[52],"avenue":[54],"decrease":[56],"the":[57,66,90,117,127,132,156,168],"rates.":[59],"Current":[60],"literature":[61],"in":[62,144,167],"utilizing":[63],"for":[65,158],"prediction":[67],"from":[70,108],"scans":[72,111],"has":[73],"shown":[74],"markers":[76,149],"adiposity":[78],"strong":[80],"predictors":[81],"diabetes;":[83],"however,":[84],"we":[85,122,139],"aim":[86],"determine":[88],"if":[89],"pancreas":[91],"alone":[92],"sufficient":[94],"diabetes.":[97],"We":[98,119],"trained":[99],"neural":[101],"network":[102],"abdominal":[109],"using":[112,135],"just":[113],"segmentation":[115],"pancreas.":[118],"found":[120],"were":[123],"able":[124],"match":[126],"performance":[128],"state":[130],"art":[133],"algorithms":[134],"less":[136],"information":[137],"could":[140],"status":[143,172],"patient":[145],"populations":[146],"without":[147],"comorbid":[148],"increased":[151],"fat.":[152],"This":[153],"study":[154],"opens":[155],"further":[159],"analysis":[160],"deep":[162],"learning":[163],"derived":[164],"biomarkers":[166],"assessment":[169],"course.":[175]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2024-04-03T00:00:00"}
