{"id":"https://openalex.org/W2929617806","doi":"https://doi.org/10.1109/access.2019.2900053","title":"Automatic Cardiothoracic Ratio Calculation With Deep Learning","display_name":"Automatic Cardiothoracic Ratio Calculation With Deep Learning","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2929617806","doi":"https://doi.org/10.1109/access.2019.2900053","mag":"2929617806"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2900053","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2900053","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08675927.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08675927.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101652594","display_name":"Zhennan Li","orcid":"https://orcid.org/0009-0008-5886-1651"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhennan Li","raw_affiliation_strings":["Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108105408","display_name":"Zhihui Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihui Hou","raw_affiliation_strings":["Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418433","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0002-3525-9755"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["Infervision, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3525-9755","affiliations":[{"raw_affiliation_string":"Infervision, Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031089205","display_name":"Zhi Feng Hao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Hao","raw_affiliation_strings":["Infervision, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Infervision, Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056813093","display_name":"Yunqiang An","orcid":null},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunqiang An","raw_affiliation_strings":["Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100745999","display_name":"Sen Liang","orcid":"https://orcid.org/0000-0001-5511-2614"},"institutions":[{"id":"https://openalex.org/I4210111607","display_name":"InferVision (China)","ror":"https://ror.org/027h3dg90","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210111607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Liang","raw_affiliation_strings":["Infervision, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5511-2614","affiliations":[{"raw_affiliation_string":"Infervision, Beijing, China","institution_ids":["https://openalex.org/I4210111607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101663592","display_name":"Bin Lu","orcid":"https://orcid.org/0000-0002-5815-4972"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Lu","raw_affiliation_strings":["Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":5.2824,"has_fulltext":true,"cited_by_count":60,"citation_normalized_percentile":{"value":0.95957236,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"37749","last_page":"37756"},"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":0.9966999888420105,"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":0.9966999888420105,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9965999722480774,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/deep-learning","display_name":"Deep learning","score":0.8350337147712708},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7266463041305542},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6257738471031189},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.5499153733253479},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4846106469631195},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4503728449344635},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43964868783950806},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3488905429840088},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33419036865234375},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.33119601011276245},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32072657346725464},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.28338128328323364}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8350337147712708},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7266463041305542},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6257738471031189},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.5499153733253479},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4846106469631195},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4503728449344635},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43964868783950806},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3488905429840088},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33419036865234375},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.33119601011276245},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32072657346725464},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.28338128328323364},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2900053","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2900053","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08675927.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:bf57a20d56c9439facc871c227e9784c","is_oa":true,"landing_page_url":"https://doaj.org/article/bf57a20d56c9439facc871c227e9784c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 37749-37756 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2900053","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2900053","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08675927.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.7099999785423279,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2929617806.pdf","grobid_xml":"https://content.openalex.org/works/W2929617806.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1533861849","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W2018153793","https://openalex.org/W2093268473","https://openalex.org/W2105873812","https://openalex.org/W2127890285","https://openalex.org/W2161236525","https://openalex.org/W2163605009","https://openalex.org/W2186615578","https://openalex.org/W2310992461","https://openalex.org/W2323929895","https://openalex.org/W2332066482","https://openalex.org/W2440599146","https://openalex.org/W2493683088","https://openalex.org/W2530279937","https://openalex.org/W2559794190","https://openalex.org/W2589644515","https://openalex.org/W2617669016","https://openalex.org/W2732063980","https://openalex.org/W2767236661","https://openalex.org/W2898859377","https://openalex.org/W2919115771","https://openalex.org/W2952793010","https://openalex.org/W2963544187","https://openalex.org/W2997422821","https://openalex.org/W3104258355","https://openalex.org/W6629815555","https://openalex.org/W6631943919","https://openalex.org/W6639824700","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2372194214","https://openalex.org/W3081640970","https://openalex.org/W4200439127","https://openalex.org/W2021013714"],"abstract_inverted_index":{"Deep":[0],"learning":[1,16,27,58,96,106,132,156,190],"is":[2],"a":[3,25],"growing":[4],"trend":[5],"in":[6,19,34,239],"medical":[7],"image":[8],"analysis.":[9],"There":[10],"are":[11],"limited":[12],"data":[13],"of":[14,80,86,123,162,219,235],"deep":[15,26,57,95,105,131,155,189],"techniques":[17],"applied":[18],"Chest":[20],"X-rays.":[21,36],"This":[22],"paper":[23],"proposed":[24],"algorithm":[28],"for":[29,83],"cardiothoracic":[30],"ratio":[31],"(CTR)":[32],"calculation":[33],"chest":[35,46],"A":[37],"fully":[38],"convolutional":[39],"neural":[40],"network":[41],"was":[42,89,136,144,157,223],"employed":[43],"to":[44,126,225],"segment":[45],"X-ray":[47],"images":[48],"and":[49,69,73,91,98,133,141,178,195,205,230],"calculate":[50],"CTR.":[51],"CTR":[52,100,240],"values":[53,101],"derived":[54,102],"from":[55,103],"the":[56,63,81,84,104,112,163,185,215,236],"model":[59,82],"were":[60,182],"compared":[61,92,213],"with":[62,93,111,115,214],"reference":[64,113,134],"standard":[65,135],"using":[66],"Bland-Altman":[67],"analysis":[68],"linear":[70],"correlation":[71,75],"graphs,":[72],"intra-class":[74],"(ICC)":[76],"analyses.":[77],"Diagnostic":[78,175],"performance":[79],"detection":[85],"heart":[87],"enlargement":[88],"assessed":[90],"other":[94],"methods":[97],"radiologists.":[99],"method":[107,165,238],"showed":[108,191],"excellent":[109],"agreement":[110,124],"standard,":[114],"mean":[116],"difference":[117],"0.0004":[118],"\u00b1":[119],"0.0133,":[120],"95%":[121],"limits":[122],"-0.0256":[125],"0.0264.":[127],"Correlation":[128],"coefficient":[129,143],"between":[130,184],"0.965":[137],"(P":[138,149],"<;":[139,150,173],"0.001),":[140],"ICC":[142],"0.982":[145],"(95%":[146],"CI":[147],"0.978-0.985)":[148],"0.001).":[151],"Measurement":[152],"time":[153,229],"by":[154],"significantly":[158],"less":[159],"than":[160,233],"that":[161,234],"manual":[164,216,237],"[0.69":[166],"(0.69-0.70)":[167],"VS":[168],"25.26":[169],"(23.49-27.44)":[170],"seconds,":[171],"P":[172,202,209],"0.001].":[174],"accuracy,":[176],"specificity,":[177],"positive":[179],"predictive":[180,197],"value":[181,198],"comparable":[183],"two":[186],"methods.":[187],"However,":[188],"relatively":[192],"higher":[193],"sensitivity":[194],"negative":[196],"(97.2%":[199],"vs":[200,207],"91.4%,":[201],"=":[203,210],"0.004;":[204],"96.0%":[206],"89.0%,":[208],"0.006;":[211],"respectively)":[212],"method.":[217],"Performance":[218],"this":[220],"computer-aided":[221],"technique":[222],"demonstrated":[224],"be":[226],"more":[227],"reliable,":[228],"labor":[231],"saving":[232],"calculation.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T08:27:34.040176","created_date":"2025-10-10T00:00:00"}
