{"id":"https://openalex.org/W3207253289","doi":"https://doi.org/10.23919/wac50355.2021.9559487","title":"Comparison of Machine Learning Methods for Predicting Modified Total Shape Score in X-ray Radiography","display_name":"Comparison of Machine Learning Methods for Predicting Modified Total Shape Score in X-ray Radiography","publication_year":2021,"publication_date":"2021-08-01","ids":{"openalex":"https://openalex.org/W3207253289","doi":"https://doi.org/10.23919/wac50355.2021.9559487","mag":"3207253289"},"language":"en","primary_location":{"id":"doi:10.23919/wac50355.2021.9559487","is_oa":false,"landing_page_url":"https://doi.org/10.23919/wac50355.2021.9559487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 World Automation Congress (WAC)","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/A5076974965","display_name":"Kohei Nakatsu","orcid":null},"institutions":[{"id":"https://openalex.org/I180941496","display_name":"University of Hyogo","ror":"https://ror.org/0151bmh98","country_code":"JP","type":"education","lineage":["https://openalex.org/I180941496"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kohei Nakatsu","raw_affiliation_strings":["University of Hyogo, Himeji, Japan"],"affiliations":[{"raw_affiliation_string":"University of Hyogo, Himeji, Japan","institution_ids":["https://openalex.org/I180941496"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040874965","display_name":"Kento Morita","orcid":"https://orcid.org/0000-0002-7171-8197"},"institutions":[{"id":"https://openalex.org/I178574317","display_name":"Mie University","ror":"https://ror.org/01529vy56","country_code":"JP","type":"education","lineage":["https://openalex.org/I178574317"]},{"id":"https://openalex.org/I2799546942","display_name":"Tsu City College","ror":"https://ror.org/01gjxh181","country_code":"JP","type":"education","lineage":["https://openalex.org/I2799546942"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kento Morita","raw_affiliation_strings":["Mie University, Tsu, Japan"],"affiliations":[{"raw_affiliation_string":"Mie University, Tsu, Japan","institution_ids":["https://openalex.org/I2799546942","https://openalex.org/I178574317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101617378","display_name":"Daisuke Fujita","orcid":"https://orcid.org/0000-0002-0157-145X"},"institutions":[{"id":"https://openalex.org/I180941496","display_name":"University of Hyogo","ror":"https://ror.org/0151bmh98","country_code":"JP","type":"education","lineage":["https://openalex.org/I180941496"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Daisuke Fujita","raw_affiliation_strings":["University of Hyogo, Himeji, Japan"],"affiliations":[{"raw_affiliation_string":"University of Hyogo, Himeji, Japan","institution_ids":["https://openalex.org/I180941496"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042142624","display_name":"Syoji Kobashi","orcid":"https://orcid.org/0000-0003-3659-4114"},"institutions":[{"id":"https://openalex.org/I180941496","display_name":"University of Hyogo","ror":"https://ror.org/0151bmh98","country_code":"JP","type":"education","lineage":["https://openalex.org/I180941496"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Syoji Kobashi","raw_affiliation_strings":["University of Hyogo, Himeji, Japan"],"affiliations":[{"raw_affiliation_string":"University of Hyogo, Himeji, Japan","institution_ids":["https://openalex.org/I180941496"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5076974965"],"corresponding_institution_ids":["https://openalex.org/I180941496"],"apc_list":null,"apc_paid":null,"fwci":0.0836,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38979965,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"86","last_page":"91"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9423999786376953,"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"}},"topics":[{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9423999786376953,"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"}},{"id":"https://openalex.org/T12800","display_name":"Musculoskeletal synovial abnormalities and treatments","score":0.939300000667572,"subfield":{"id":"https://openalex.org/subfields/2745","display_name":"Rheumatology"},"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":0.9366000294685364,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/rheumatoid-arthritis","display_name":"Rheumatoid arthritis","score":0.7297742962837219},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7062541246414185},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.6389362812042236},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.6213768720626831},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5385791659355164},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3826574683189392},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2755856513977051},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.27479663491249084},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.24191448092460632}],"concepts":[{"id":"https://openalex.org/C2777575956","wikidata":"https://www.wikidata.org/wiki/Q187255","display_name":"Rheumatoid arthritis","level":2,"score":0.7297742962837219},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7062541246414185},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.6389362812042236},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6213768720626831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5385791659355164},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3826574683189392},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2755856513977051},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.27479663491249084},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.24191448092460632}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/wac50355.2021.9559487","is_oa":false,"landing_page_url":"https://doi.org/10.23919/wac50355.2021.9559487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 World Automation Congress (WAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2106989209","https://openalex.org/W2161969291","https://openalex.org/W2343403739","https://openalex.org/W2531409750","https://openalex.org/W2909558783","https://openalex.org/W2933814040","https://openalex.org/W2962835968","https://openalex.org/W2963446712","https://openalex.org/W3094028714"],"related_works":["https://openalex.org/W2185550546","https://openalex.org/W2416097977","https://openalex.org/W2028766184","https://openalex.org/W2993678931","https://openalex.org/W2384738060","https://openalex.org/W3045583548","https://openalex.org/W812086887","https://openalex.org/W2101475263","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"It":[0,43],"is":[1],"predicted":[2],"that":[3,117],"there":[4],"are":[5],"600,000":[6],"to":[7,49],"1,000,000":[8],"patients":[9,106],"with":[10,125,135],"rheumatoid":[11,89],"arthritis":[12,90],"(RA)":[13],"in":[14],"Japan.":[15],"To":[16,94],"quantitatively":[17],"diagnose":[18],"RA":[19,56,105],"using":[20,65,71,107],"X-ray":[21,108],"images,":[22],"the":[23,33,38,41,51,92,96,118,128],"modified":[24],"total":[25],"sharp":[26],"score":[27],"(mTSS)":[28],"has":[29],"been":[30],"used,":[31],"although,":[32],"evaluation":[34],"method":[35,64],"depends":[36],"on":[37,83,103],"experience":[39],"of":[40,53,78,91,110,121,131],"physicians.":[42],"desires":[44],"computer-aided":[45],"diagnosis":[46,54],"(CAD)":[47],"systems":[48],"improve":[50],"quality":[52],"for":[55,87],"patients.":[57],"In":[58],"this":[59],"study,":[60],"we":[61,99],"compare":[62,95],"a":[63],"ridge":[66],"regression":[67],"(RR)":[68],"and":[69,76,127],"methods":[70,86],"3":[72],"models":[73],"(VGG16,":[74],"DenseNet201":[75],"Xception)":[77],"convolutional":[79],"neural":[80],"network":[81],"(CNN)":[82],"mTSS":[84,119,129],"prediction":[85,120,130],"hand":[88],"hand.":[93],"4":[97],"method,":[98],"conducted":[100],"an":[101],"experiment":[102],"90":[104],"images":[109],"their":[111],"hands.":[112],"The":[113],"experimental":[114],"results":[115],"showed":[116],"erosion":[122],"was":[123,133],"best":[124,134],"RR,":[126],"JSN":[132],"VGG16.":[136]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
