{"id":"https://openalex.org/W4385387692","doi":"https://doi.org/10.48550/arxiv.2307.14859","title":"Comparative Evaluation of Digital and Analog Chest Radiographs to Identify Tuberculosis using Deep Learning Model","display_name":"Comparative Evaluation of Digital and Analog Chest Radiographs to Identify Tuberculosis using Deep Learning Model","publication_year":2023,"publication_date":"2023-07-27","ids":{"openalex":"https://openalex.org/W4385387692","doi":"https://doi.org/10.48550/arxiv.2307.14859"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2307.14859","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.14859","pdf_url":"https://arxiv.org/pdf/2307.14859","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2307.14859","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047348099","display_name":"Subhankar Chattoraj","orcid":"https://orcid.org/0000-0003-1218-0769"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chattoraj, Subhankar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024079803","display_name":"Bhargava Reddy","orcid":"https://orcid.org/0000-0001-7643-7346"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reddy, Bhargava","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051647145","display_name":"Manoj Tadepalli","orcid":"https://orcid.org/0000-0002-8632-9231"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tadepalli, Manoj","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087649963","display_name":"Preetham Putha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Putha, Preetham","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047348099"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9873999953269958,"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/T10862","display_name":"AI in cancer detection","score":0.9677000045776367,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/radiological-weapon","display_name":"Radiological weapon","score":0.7698028087615967},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.7460370659828186},{"id":"https://openalex.org/keywords/tuberculosis","display_name":"Tuberculosis","score":0.6048200130462646},{"id":"https://openalex.org/keywords/pulmonary-tuberculosis","display_name":"Pulmonary tuberculosis","score":0.5713233351707458},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.5608831644058228},{"id":"https://openalex.org/keywords/radiological-imaging","display_name":"Radiological imaging","score":0.5278934240341187},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.47672000527381897},{"id":"https://openalex.org/keywords/significant-difference","display_name":"Significant difference","score":0.4281983971595764},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.4060976803302765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37893861532211304},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.35377198457717896},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.35368868708610535},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.1825457513332367},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.1728862226009369},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.13448014855384827}],"concepts":[{"id":"https://openalex.org/C190892606","wikidata":"https://www.wikidata.org/wiki/Q3512464","display_name":"Radiological weapon","level":2,"score":0.7698028087615967},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.7460370659828186},{"id":"https://openalex.org/C2781069245","wikidata":"https://www.wikidata.org/wiki/Q12204","display_name":"Tuberculosis","level":2,"score":0.6048200130462646},{"id":"https://openalex.org/C2908628106","wikidata":"https://www.wikidata.org/wiki/Q12204","display_name":"Pulmonary tuberculosis","level":3,"score":0.5713233351707458},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.5608831644058228},{"id":"https://openalex.org/C2994024180","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Radiological imaging","level":2,"score":0.5278934240341187},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.47672000527381897},{"id":"https://openalex.org/C3018023364","wikidata":"https://www.wikidata.org/wiki/Q425265","display_name":"Significant difference","level":2,"score":0.4281983971595764},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.4060976803302765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37893861532211304},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.35377198457717896},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.35368868708610535},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.1825457513332367},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1728862226009369},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.13448014855384827},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2307.14859","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.14859","pdf_url":"https://arxiv.org/pdf/2307.14859","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2307.14859","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2307.14859","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2307.14859","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.14859","pdf_url":"https://arxiv.org/pdf/2307.14859","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385387692.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2131890314","https://openalex.org/W4206658826","https://openalex.org/W3173632689","https://openalex.org/W3208510144","https://openalex.org/W2330494972","https://openalex.org/W2402613944","https://openalex.org/W2412867219","https://openalex.org/W4214604815","https://openalex.org/W4315753308","https://openalex.org/W142245164"],"abstract_inverted_index":{"Purpose:Chest":[0],"X-ray":[1],"(CXR)":[2],"is":[3,45,60,193],"an":[4,38,177],"essential":[5],"tool":[6],"and":[7,32,50,57,74,94,111,198,220],"one":[8],"of":[9,22,35,43,53,69,90,97,148,152,157,169,174,185,207,215],"the":[10,30,51,67,98,141,160,180,183,190,205,208],"most":[11],"prescribed":[12],"imaging":[13,26],"to":[14,130,139],"detect":[15],"pulmonary":[16],"abnormalities,":[17],"with":[18,101,115,166,189],"a":[19,54,167,172],"yearly":[20],"estimate":[21],"over":[23],"2":[24],"billion":[25],"performed":[27],"worldwide.":[28],"However,":[29],"accurate":[31],"timely":[33],"diagnosis":[34],"TB":[36,44,216],"remains":[37],"unmet":[39],"goal.":[40],"The":[41,77,150,201],"prevalence":[42],"highest":[46],"in":[47,84,144,182,211,217],"low-middle-income":[48],"countries,":[49],"requirement":[52],"portable,":[55],"automated,":[56],"reliable":[58],"solution":[59],"required.":[61],"In":[62],"this":[63],"study,":[64],"we":[65],"compared":[66],"performance":[68],"DL-based":[70,79,142,209],"devices":[71],"on":[72,159],"digital":[73,219],"analog":[75,221],"CXR.":[76,222],"evaluated":[78],"device":[80,143,210],"can":[81],"be":[82],"used":[83],"resource-constraint":[85],"settings.":[86],"Methods:":[87],"A":[88],"total":[89],"10,000":[91,134],"CXR":[92],"DICOMs(.dcm)":[93],"printed":[95],"photos":[96],"films":[99],"acquired":[100],"three":[102,186],"different":[103],"cellular":[104,187],"phones":[105],"-":[106],"Samsung":[107],"S8,":[108],"iPhone":[109,112],"8,":[110],"XS":[113],"along":[114],"their":[116],"radiological":[117,146,213],"report":[118],"were":[119,137],"retrospectively":[120],"collected":[121],"from":[122,127],"various":[123],"sites":[124],"across":[125],"India":[126],"April":[128],"2020":[129],"March":[131],"2021.":[132],"Results:":[133],"chest":[135],"X-rays":[136],"utilized":[138],"evaluate":[140],"identifying":[145,212],"signs":[147,156,214],"TB.":[149],"AUC":[151,184],"qXR":[153],"for":[154],"detecting":[155],"tuberculosis":[158],"original":[161,191],"DICOMs":[162,192],"dataset":[163],"was":[164],"0.928":[165],"sensitivity":[168],"0.841":[170],"at":[171],"specificity":[173],"0.806.":[175],"At":[176],"optimal":[178],"threshold,":[179],"difference":[181,203],"smartphones":[188],"0.024":[194],"(2.55%),":[195],"0.048":[196],"(5.10%),":[197],"0.038":[199],"(1.91%).":[200],"minimum":[202],"demonstrates":[204],"robustness":[206],"both":[218]},"counts_by_year":[],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2023-07-30T00:00:00"}