{"id":"https://openalex.org/W3154384675","doi":"https://doi.org/10.1117/12.2582307","title":"Investigating Covid-19 pandemic-induced effects on detection of emergent clinical imaging findings by large-scale tracking of utilization and reading results for AI-based image analysis services","display_name":"Investigating Covid-19 pandemic-induced effects on detection of emergent clinical imaging findings by large-scale tracking of utilization and reading results for AI-based image analysis services","publication_year":2021,"publication_date":"2021-02-12","ids":{"openalex":"https://openalex.org/W3154384675","doi":"https://doi.org/10.1117/12.2582307","mag":"3154384675"},"language":"en","primary_location":{"id":"doi:10.1117/12.2582307","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2582307","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging","raw_type":"proceedings-article"},"type":"article","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/A5036396088","display_name":"Axel Wism\u00fcller","orcid":null},"institutions":[{"id":"https://openalex.org/I28792581","display_name":"University of Rochester Medical Center","ror":"https://ror.org/00trqv719","country_code":"US","type":"funder","lineage":["https://openalex.org/I28792581","https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Axel Wism\u00fcller","raw_affiliation_strings":["Univ. of Rochester Medical Ctr. (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Rochester Medical Ctr. (United States)","institution_ids":["https://openalex.org/I28792581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049278342","display_name":"Larry Stockmaster","orcid":"https://orcid.org/0000-0002-3596-0787"},"institutions":[{"id":"https://openalex.org/I28792581","display_name":"University of Rochester Medical Center","ror":"https://ror.org/00trqv719","country_code":"US","type":"funder","lineage":["https://openalex.org/I28792581","https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Larry Stockmaster","raw_affiliation_strings":["Univ. of Rochester Medical Ctr. (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Rochester Medical Ctr. (United States)","institution_ids":["https://openalex.org/I28792581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103955616","display_name":"M. Ali Vosoughi","orcid":null},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. Ali Vosoughi","raw_affiliation_strings":["Univ. of Rochester (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Rochester (United States)","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036396088"],"corresponding_institution_ids":["https://openalex.org/I28792581"],"apc_list":null,"apc_paid":null,"fwci":0.089,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.43515354,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"26","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11296","display_name":"COVID-19 and healthcare impacts","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T11296","display_name":"COVID-19 and healthcare impacts","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9850999712944031,"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/T11368","display_name":"Long-Term Effects of COVID-19","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.5601593852043152},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.53146892786026},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.5159876346588135},{"id":"https://openalex.org/keywords/outbreak","display_name":"Outbreak","score":0.49854612350463867},{"id":"https://openalex.org/keywords/pandemic","display_name":"Pandemic","score":0.49374517798423767},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47316914796829224},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.46659162640571594},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.4545856714248657},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45117655396461487},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.42304620146751404},{"id":"https://openalex.org/keywords/medical-emergency","display_name":"Medical emergency","score":0.3473513722419739},{"id":"https://openalex.org/keywords/emergency-medicine","display_name":"Emergency medicine","score":0.32379990816116333},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.19550946354866028},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.1352117955684662},{"id":"https://openalex.org/keywords/infectious-disease","display_name":"Infectious disease (medical specialty)","score":0.126207172870636},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.12028190493583679},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.11016759276390076},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09710514545440674}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5601593852043152},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.53146892786026},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.5159876346588135},{"id":"https://openalex.org/C116675565","wikidata":"https://www.wikidata.org/wiki/Q3241045","display_name":"Outbreak","level":2,"score":0.49854612350463867},{"id":"https://openalex.org/C89623803","wikidata":"https://www.wikidata.org/wiki/Q12184","display_name":"Pandemic","level":5,"score":0.49374517798423767},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47316914796829224},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.46659162640571594},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4545856714248657},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45117655396461487},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.42304620146751404},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.3473513722419739},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.32379990816116333},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.19550946354866028},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.1352117955684662},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.126207172870636},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.12028190493583679},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.11016759276390076},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09710514545440674},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2582307","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2582307","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8299999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W172813713","https://openalex.org/W775277085","https://openalex.org/W1527829859","https://openalex.org/W1531716744","https://openalex.org/W1580161078","https://openalex.org/W1964107394","https://openalex.org/W1965481579","https://openalex.org/W1971575144","https://openalex.org/W1972228177","https://openalex.org/W1975607784","https://openalex.org/W1978215483","https://openalex.org/W1997269994","https://openalex.org/W2011988161","https://openalex.org/W2013235441","https://openalex.org/W2017345738","https://openalex.org/W2020254800","https://openalex.org/W2034476862","https://openalex.org/W2034643755","https://openalex.org/W2059664105","https://openalex.org/W2060721178","https://openalex.org/W2060776942","https://openalex.org/W2061757746","https://openalex.org/W2061957656","https://openalex.org/W2092481496","https://openalex.org/W2096545550","https://openalex.org/W2102292745","https://openalex.org/W2103961356","https://openalex.org/W2104024771","https://openalex.org/W2109574033","https://openalex.org/W2114397654","https://openalex.org/W2130107054","https://openalex.org/W2139404519","https://openalex.org/W2141317097","https://openalex.org/W2148585001","https://openalex.org/W2149018240","https://openalex.org/W2169397963","https://openalex.org/W2171546147","https://openalex.org/W2172180476","https://openalex.org/W2176037610","https://openalex.org/W2332715186","https://openalex.org/W2335517681","https://openalex.org/W2341759933","https://openalex.org/W2464525596","https://openalex.org/W2593687779","https://openalex.org/W2597194388","https://openalex.org/W2626279430","https://openalex.org/W2775336355","https://openalex.org/W2791864097","https://openalex.org/W2803807974","https://openalex.org/W2910357997","https://openalex.org/W2963120588","https://openalex.org/W3008367040","https://openalex.org/W3013398803","https://openalex.org/W3015863623","https://openalex.org/W3015946392","https://openalex.org/W3019953082","https://openalex.org/W3020954032","https://openalex.org/W3035517854","https://openalex.org/W3084005084","https://openalex.org/W3099014884","https://openalex.org/W3102306183","https://openalex.org/W3129992072","https://openalex.org/W3130400782","https://openalex.org/W3130430430","https://openalex.org/W3132029409","https://openalex.org/W4231106891","https://openalex.org/W4241857248","https://openalex.org/W4294398017"],"related_works":["https://openalex.org/W2373635223","https://openalex.org/W2412355096","https://openalex.org/W1990012352","https://openalex.org/W2431766951","https://openalex.org/W4385969441","https://openalex.org/W127458931","https://openalex.org/W2362266265","https://openalex.org/W3028429280","https://openalex.org/W2557977292","https://openalex.org/W120499389"],"abstract_inverted_index":{"We":[0,140],"introduce":[1],"a":[2,84,126,151,162,245],"method":[3],"for":[4,21,43,74,97,104],"tracking":[5,29,224],"results":[6,35,137,220,230],"and":[7,33,50,133,168,228],"utilization":[8,32,227],"of":[9,47,54,100,183,248],"Artificial":[10],"Intelligence":[11],"(tru-AI)":[12],"based":[13],"on":[14,25,80,251],"machine":[15,129],"learning":[16],"applications":[17],"in":[18,108,130],"medical":[19],"imaging,":[20],"analyzing":[22],"pandemic-induced":[23],"effects":[24,250],"healthcare":[26,154],"systems.":[27],"By":[28],"both":[30,225],"large-scale":[31,226],"AI":[34,86,229],"data,":[36,231],"the":[37,45,52,171,199,215,232],"tru-AI":[38,233],"approach":[39,234],"can":[40,235],"establish":[41],"surrogates":[42],"measuring":[44],"amount":[46],"care":[48],"provided":[49],"estimate":[51],"prevalence":[53],"certain":[55],"disease":[56],"conditions":[57],"under":[58],"unusual":[59],"circumstances,":[60],"such":[61,114],"as":[62,115,239],"pandemic":[63],"outbreaks.":[64],"To":[65],"quantitatively":[66],"evaluate":[67],"our":[68],"approach,":[69],"we":[70,202],"analyzed":[71],"service":[72],"requests":[73],"automatically":[75],"identifying":[76],"intracranial":[77],"hemorrhage":[78],"(ICH)":[79],"head":[81,147],"CT":[82,148],"using":[83],"commercial":[85],"solution":[87],"(Aidoc,":[88],"Tel":[89],"Aviv,":[90],"Israel).":[91],"This":[92],"software":[93],"is":[94,122],"typically":[95],"used":[96],"AI-based":[98,134],"prioritization":[99],"radiologists\u2019":[101],"reading":[102],"lists":[103],"reducing":[105],"turnaround":[106],"times":[107],"patients":[109,186],"with":[110],"emergent":[111],"clinical":[112,237],"findings,":[113],"ICH":[116,135,205],"or":[117],"pulmonary":[118],"embolism.":[119],"Imaging":[120],"data":[121],"anonymized,":[123],"uploaded":[124],"to":[125,209],"cloud-based":[127],"inference":[128],"real":[131],"time,":[132],"detection":[136],"are":[138],"returned.":[139],"recorded":[141],"N":[142],"=":[143],"3,084":[144],"emergency-setting":[145],"non-contrast":[146],"studies":[149],"at":[150,244],"major":[152],"US":[153],"system":[155],"during":[156,190,212],"two":[157],"observation":[158],"periods,":[159],"namely":[160],"(i)":[161],"pre-pandemic":[163],"epoch":[164],"(January":[165],"1\u201331,":[166],"2020)":[167],"(ii)":[169],"after":[170],"Covid-19":[172,200,216],"outbreak":[173,217],"(March":[174],"15":[175],"\u2013":[176],"April":[177],"30,":[178],"2020).":[179],"Although":[180],"daily":[181],"counts":[182],"unique":[184],"imaged":[185],"were":[187],"significantly":[188],"lower":[189],"(37.9":[191],"±":[192,197],"7.6)":[193],"than":[194,213],"before":[195,214],"(42.0":[196],"6.2)":[198],"outbreak,":[201],"found":[203],"that":[204],"was":[206],"more":[207],"likely":[208],"be":[210],"observed":[211],"(p<0.05).":[218],"Our":[219],"suggest":[221],"that,":[222],"by":[223],"contribute":[236],"value":[238],"an":[240],"exploratory":[241],"tool,":[242],"aiming":[243],"better":[246],"understanding":[247],"pandemic-related":[249],"healthcare.":[252]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}