{"id":"https://openalex.org/W4406328993","doi":"https://doi.org/10.1145/3706890.3706937","title":"A hybrid model for the clinical manifestation severity assessment of COVID-19 based on Chest CT Images","display_name":"A hybrid model for the clinical manifestation severity assessment of COVID-19 based on Chest CT Images","publication_year":2024,"publication_date":"2024-08-13","ids":{"openalex":"https://openalex.org/W4406328993","doi":"https://doi.org/10.1145/3706890.3706937"},"language":"en","primary_location":{"id":"doi:10.1145/3706890.3706937","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3706890.3706937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science","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/A5111722445","display_name":"Feng\u2010Wei Yao","orcid":"https://orcid.org/0009-0006-9593-4341"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fei Yao","raw_affiliation_strings":["School of Medicine, Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0006-9593-4341","affiliations":[{"raw_affiliation_string":"School of Medicine, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114057053","display_name":"Xinang Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099558","display_name":"Shanghai Changzheng Hospital","ror":"https://ror.org/0103dxn66","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210099558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinang Jiang","raw_affiliation_strings":["Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0009-9632-2859","affiliations":[{"raw_affiliation_string":"Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China","institution_ids":["https://openalex.org/I4210099558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100597470","display_name":"Jingyi Zhao","orcid":"https://orcid.org/0000-0002-4058-1095"},"institutions":[{"id":"https://openalex.org/I4210099558","display_name":"Shanghai Changzheng Hospital","ror":"https://ror.org/0103dxn66","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210099558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyi Zhao","raw_affiliation_strings":["Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-4058-1095","affiliations":[{"raw_affiliation_string":"Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China","institution_ids":["https://openalex.org/I4210099558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057234723","display_name":"Qianyun Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099558","display_name":"Shanghai Changzheng Hospital","ror":"https://ror.org/0103dxn66","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210099558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianyun Ma","raw_affiliation_strings":["Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0001-2476-3788","affiliations":[{"raw_affiliation_string":"Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China","institution_ids":["https://openalex.org/I4210099558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004583777","display_name":"Li Fan","orcid":"https://orcid.org/0000-0003-4722-3933"},"institutions":[{"id":"https://openalex.org/I4210099558","display_name":"Shanghai Changzheng Hospital","ror":"https://ror.org/0103dxn66","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210099558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Fan","raw_affiliation_strings":["Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-4722-3933","affiliations":[{"raw_affiliation_string":"Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China","institution_ids":["https://openalex.org/I4210099558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001901428","display_name":"Shiyuan Liu","orcid":"https://orcid.org/0000-0003-3420-0310"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyuan Liu","raw_affiliation_strings":["School of Medicine, Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-3420-0310","affiliations":[{"raw_affiliation_string":"School of Medicine, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5111722445"],"corresponding_institution_ids":["https://openalex.org/I113940042"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41826509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"265","last_page":"272"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9998999834060669,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9998999834060669,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9987999796867371,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.76906418800354},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.5212379097938538},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5169658064842224},{"id":"https://openalex.org/keywords/2019-20-coronavirus-outbreak","display_name":"2019-20 coronavirus outbreak","score":0.49537694454193115},{"id":"https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","score":0.4822614789009094},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3455306589603424},{"id":"https://openalex.org/keywords/virology","display_name":"Virology","score":0.2568780779838562},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.2320813238620758},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.05628424882888794}],"concepts":[{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.76906418800354},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.5212379097938538},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5169658064842224},{"id":"https://openalex.org/C3006700255","wikidata":"https://www.wikidata.org/wiki/Q81068910","display_name":"2019-20 coronavirus outbreak","level":3,"score":0.49537694454193115},{"id":"https://openalex.org/C3007834351","wikidata":"https://www.wikidata.org/wiki/Q82069695","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","level":5,"score":0.4822614789009094},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3455306589603424},{"id":"https://openalex.org/C159047783","wikidata":"https://www.wikidata.org/wiki/Q7215","display_name":"Virology","level":1,"score":0.2568780779838562},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.2320813238620758},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.05628424882888794},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C116675565","wikidata":"https://www.wikidata.org/wiki/Q3241045","display_name":"Outbreak","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3706890.3706937","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3706890.3706937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1581559513","https://openalex.org/W2884436604","https://openalex.org/W2962934715","https://openalex.org/W2963446712","https://openalex.org/W3004906315","https://openalex.org/W3013585706","https://openalex.org/W3054666633","https://openalex.org/W3103199493","https://openalex.org/W3120191671","https://openalex.org/W3126296879","https://openalex.org/W3196559000","https://openalex.org/W3208176997","https://openalex.org/W3210363615","https://openalex.org/W4210344342","https://openalex.org/W4385658182"],"related_works":["https://openalex.org/W3036314732","https://openalex.org/W3009669391","https://openalex.org/W3176864053","https://openalex.org/W4206669628","https://openalex.org/W3171943759","https://openalex.org/W4292098121","https://openalex.org/W3154141118","https://openalex.org/W4388896133","https://openalex.org/W3031607536","https://openalex.org/W3198183218"],"abstract_inverted_index":{"Severity":[0],"assessment":[1,22],"of":[2,30,51,164],"COVID-19":[3,83],"patients":[4,57,96],"is":[5,41],"vitally":[6],"important,":[7],"and":[8,47,58,75,106,129,160,168],"many":[9],"deep":[10],"learning":[11],"models":[12,23],"have":[13],"been":[14],"created":[15],"for":[16,56,148],"this":[17],"task.":[18],"However,":[19],"current":[20],"severity":[21],"predominantly":[24],"focus":[25],"on":[26,101,127,134],"the":[27,35,42,91,109,153],"quantitative":[28],"measurement":[29],"lung":[31],"infections,":[32],"often":[33],"neglecting":[34],"direct":[36],"clinical":[37,104],"manifestations":[38,105],"assessment,":[39],"which":[40],"aspect":[43],"that":[44],"nursing":[45],"staff":[46],"frontline":[48],"physicians":[49],"deem":[50],"utmost":[52],"importance":[53],"when":[54],"caring":[55],"devising":[59],"treatment":[60],"strategies.":[61],"We":[62],"retrospectively":[63],"implemented":[64],"a":[65,123,130],"new":[66],"artificial":[67],"intelligence":[68],"model":[69,124,131,140],"combining":[70],"SwinUNETR":[71,128],"(a":[72,78],"segmentation":[73],"network)":[74,80],"3D":[76,135],"ResNet50":[77],"classification":[79,116],"using":[81],"573":[82],"patients'":[84],"computerized":[85],"tomography":[86],"(CT)":[87],"scan":[88],"images.":[89],"In":[90],"implementation":[92],"process,":[93],"we":[94,120],"classified":[95],"into":[97,111],"three":[98],"classes":[99],"based":[100,125,132],"their":[102],"actual":[103],"further":[107],"decomposed":[108],"problem":[110],"two":[112,146],"stages":[113,147],"to":[114],"enhance":[115],"accuracy.":[117],"For":[118],"comparison,":[119],"also":[121],"prepared":[122],"only":[126,133],"ResNet50.":[136],"Our":[137],"proposed":[138],"hybrid":[139],"achieved":[141],"better":[142],"overall":[143],"performance":[144],"in":[145],"metrics":[149],"like":[150],"areas":[151],"under":[152],"receiver":[154],"operating":[155],"characteristic":[156],"curves":[157],"(AUROC),":[158],"accuracy,":[159],"recall,":[161],"with":[162],"values":[163],"0.905,":[165],"84.75%,":[166],"82.76%,":[167,170],"0.747,":[169],"57.14%,":[171],"respectively.":[172]},"counts_by_year":[],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}