{"id":"https://openalex.org/W4220743632","doi":"https://doi.org/10.1117/12.2608426","title":"Multi-stage investigation of deep neural networks for COVID-19 B-line feature detection in simulated and in vivo ultrasound images","display_name":"Multi-stage investigation of deep neural networks for COVID-19 B-line feature detection in simulated and in vivo ultrasound images","publication_year":2022,"publication_date":"2022-04-01","ids":{"openalex":"https://openalex.org/W4220743632","doi":"https://doi.org/10.1117/12.2608426"},"language":"en","primary_location":{"id":"doi:10.1117/12.2608426","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2608426","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"Medical Imaging 2022: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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 2022: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102898887","display_name":"Benjamin Frey","orcid":"https://orcid.org/0000-0002-2580-2638"},"institutions":[{"id":"https://openalex.org/I161515732","display_name":"University of St. Thomas - Minnesota","ror":"https://ror.org/05vfxvp80","country_code":"US","type":"education","lineage":["https://openalex.org/I161515732"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ben Frey","raw_affiliation_strings":["Univ. of St. Thomas (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of St. Thomas (United States)","institution_ids":["https://openalex.org/I161515732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074800190","display_name":"Lingyi Zhao","orcid":"https://orcid.org/0000-0001-8544-3326"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lingyi Zhao","raw_affiliation_strings":["Johns Hopkins Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins Univ. (United States)","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053345315","display_name":"Tiffany Fong","orcid":"https://orcid.org/0000-0003-2348-2340"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]},{"id":"https://openalex.org/I2799853436","display_name":"Johns Hopkins Medicine","ror":"https://ror.org/037zgn354","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799853436"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tiffany C. Fong","raw_affiliation_strings":["Johns Hopkins Medicine (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins Medicine (United States)","institution_ids":["https://openalex.org/I145311948","https://openalex.org/I2799853436"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003188378","display_name":"Muyinatu A. Lediju Bell","orcid":"https://orcid.org/0000-0002-8394-4482"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Muyinatu A. Lediju Bell","raw_affiliation_strings":["Johns Hopkins Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins Univ. (United States)","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.2295,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.9122807,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"323","issue":null,"first_page":"13","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11738","display_name":"Ultrasound in Clinical Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2706","display_name":"Critical Care and Intensive Care Medicine"},"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/T11738","display_name":"Ultrasound in Clinical Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2706","display_name":"Critical Care and Intensive Care Medicine"},"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":0.9988999962806702,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9833999872207642,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7356926202774048},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.672462522983551},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6388359069824219},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5258011221885681},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5148107409477234},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5133141875267029},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.5011355876922607},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46765273809432983},{"id":"https://openalex.org/keywords/beamforming","display_name":"Beamforming","score":0.44596022367477417},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3598284125328064},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.15344125032424927},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.11022970080375671},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09478873014450073}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7356926202774048},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.672462522983551},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6388359069824219},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5258011221885681},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5148107409477234},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5133141875267029},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.5011355876922607},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46765273809432983},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.44596022367477417},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3598284125328064},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.15344125032424927},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.11022970080375671},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09478873014450073},{"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:10.1117/12.2608426","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2608426","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"Medical Imaging 2022: Computer-Aided Diagnosis","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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 2022: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2892912782","https://openalex.org/W2910831172","https://openalex.org/W3006082171","https://openalex.org/W3006882119","https://openalex.org/W3010604545","https://openalex.org/W3019296792","https://openalex.org/W3022437947","https://openalex.org/W3033160145","https://openalex.org/W3037233011","https://openalex.org/W3086200677","https://openalex.org/W3112906655","https://openalex.org/W4200409436","https://openalex.org/W4226466403","https://openalex.org/W4231855592","https://openalex.org/W4254696009","https://openalex.org/W4293225839","https://openalex.org/W6774326982","https://openalex.org/W6774707739","https://openalex.org/W6781804285"],"related_works":["https://openalex.org/W2963170046","https://openalex.org/W2376244802","https://openalex.org/W2923631784","https://openalex.org/W818226659","https://openalex.org/W4311414679","https://openalex.org/W2158075901","https://openalex.org/W2567141277","https://openalex.org/W3124036039","https://openalex.org/W2130785056","https://openalex.org/W2132841313"],"abstract_inverted_index":{"COVID-19":[0,32,92,174,213],"is":[1,79],"a":[2],"highly":[3],"infectious":[4],"disease":[5],"with":[6,97,132,211],"high":[7],"morbidity":[8],"and":[9,17,113,147],"mortality,":[10],"requiring":[11],"tools":[12],"to":[13,28,51,58,71,80,91,180,196,209,224],"support":[14],"rapid":[15],"triage":[16],"risk":[18],"stratification.":[19],"In":[20],"response,":[21],"deep":[22,48,88,225],"learning":[23,49,89,226],"has":[24],"demonstrated":[25],"great":[26],"potential":[27],"quicklyand":[29],"autonomously":[30],"detect":[31,181],"features":[33,193],"in":[34,169],"lung":[35],"ultrasound":[36,60,73,104,218],"B-mode":[37,61,74,125,171,204,219],"images.":[38,205],"However,":[39],"no":[40],"previous":[41],"work":[42],"considers":[43],"the":[44,65,82,116,182,222],"application":[45],"of":[46,173,185,190],"these":[47],"models":[50],"signal":[52,67],"processing":[53,68],"stages":[54,69,137],"that":[55,108,138],"occur":[56],"prior":[57],"traditional":[59],"image":[62,135],"formation.":[63],"Considering":[64],"multiple":[66],"required":[70],"achieve":[72],"images,":[75],"our":[76,87,110],"research":[77],"objective":[78],"investigate":[81],"most":[83],"appropriate":[84],"stage":[85],"for":[86,109],"approach":[90],"B-line":[93,192,197,214],"feature":[94,198,215],"detection,":[95],"starting":[96],"raw":[98,145],"channel":[99],"data":[100,150],"received":[101],"by":[102,124,200],"an":[103],"transducer.":[105],"Results":[106,206],"demonstrate":[107],"given":[111],"training":[112],"testing":[114],"configuration,":[115],"maximum":[117],"Dice":[118],"similarity":[119],"coefficient":[120],"(DSC)":[121],"was":[122,166],"produced":[123],"images":[126,172,220],"(DSC":[127],"=":[128],"0.996)":[129],"when":[130,194],"compared":[131,195],"three":[133],"alternative":[134],"formation":[136],"can":[139],"serve":[140],"as":[141,221],"network":[142,165],"inputs:":[143],"(1)":[144],"in-phase":[146],"quadrature":[148],"(IQ)":[149],"before":[151],"beamforming,":[152],"(2)":[153],"beamformed":[154],"IQ":[155,160],"data,":[156],"(3)":[157],"envelope":[158],"detected":[159],"data.":[161],"The":[162],"best-performing":[163],"simulation-trained":[164],"tested":[167],"on":[168],"vivo":[170],"patients,":[175],"ultimately":[176],"achieving":[177],"76%":[178],"accuracy":[179],"same":[183],"(82%":[184],"cases)":[186,191],"or":[187],"more":[188],"(18%":[189],"detection":[199,216],"human":[201],"observers":[202],"interpreting":[203],"are":[207],"promising":[208],"proceed":[210],"future":[212],"using":[217],"input":[223],"models.":[227]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}