{"id":"https://openalex.org/W4416961969","doi":"https://doi.org/10.1109/ist66504.2025.11268437","title":"Intelligent Diagnosis of Coronary Artery Disease Using Radiomic Features from Magnetocardiography","display_name":"Intelligent Diagnosis of Coronary Artery Disease Using Radiomic Features from Magnetocardiography","publication_year":2025,"publication_date":"2025-10-15","ids":{"openalex":"https://openalex.org/W4416961969","doi":"https://doi.org/10.1109/ist66504.2025.11268437"},"language":null,"primary_location":{"id":"doi:10.1109/ist66504.2025.11268437","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268437","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","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/A5113053894","display_name":"Mingli Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingli Yan","raw_affiliation_strings":["Beihang University,School of Instrumentation and Optoelectronic Engineering,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Instrumentation and Optoelectronic Engineering,Hangzhou,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033366419","display_name":"Xiaole Han","orcid":"https://orcid.org/0000-0001-9152-8182"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaole Han","raw_affiliation_strings":["Beihang University,School of Instrumentation and Optoelectronic Engineering,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Instrumentation and Optoelectronic Engineering,Hangzhou,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071836863","display_name":"Min Xiang","orcid":"https://orcid.org/0000-0002-0239-3392"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Xiang","raw_affiliation_strings":["Beihang University,School of Instrumentation and Optoelectronic Engineering,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Instrumentation and Optoelectronic Engineering,Hangzhou,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100364769","display_name":"Jin Li","orcid":"https://orcid.org/0000-0001-9201-2321"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Li","raw_affiliation_strings":["Beihang University,School of Instrumentation and Optoelectronic Engineering,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Instrumentation and Optoelectronic Engineering,Hangzhou,China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.43220001459121704,"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/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.43220001459121704,"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":0.37560001015663147,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.03220000118017197,"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/magnetocardiography","display_name":"Magnetocardiography","score":0.9391999840736389},{"id":"https://openalex.org/keywords/coronary-artery-disease","display_name":"Coronary artery disease","score":0.6741999983787537},{"id":"https://openalex.org/keywords/cad","display_name":"CAD","score":0.5902000069618225},{"id":"https://openalex.org/keywords/radiomics","display_name":"Radiomics","score":0.4291999936103821},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.414000004529953},{"id":"https://openalex.org/keywords/stenosis","display_name":"Stenosis","score":0.38940000534057617},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.3617999851703644},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.3416000008583069}],"concepts":[{"id":"https://openalex.org/C2778475581","wikidata":"https://www.wikidata.org/wiki/Q1884389","display_name":"Magnetocardiography","level":2,"score":0.9391999840736389},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.7343000173568726},{"id":"https://openalex.org/C2778213512","wikidata":"https://www.wikidata.org/wiki/Q844935","display_name":"Coronary artery disease","level":2,"score":0.6741999983787537},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.5902000069618225},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.5478000044822693},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.48989999294281006},{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.4291999936103821},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.4162999987602234},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C2780007028","wikidata":"https://www.wikidata.org/wiki/Q2343082","display_name":"Stenosis","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32749998569488525},{"id":"https://openalex.org/C2776820930","wikidata":"https://www.wikidata.org/wiki/Q9655","display_name":"Artery","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C2779549770","wikidata":"https://www.wikidata.org/wiki/Q1122413","display_name":"Computer-aided diagnosis","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C63527458","wikidata":"https://www.wikidata.org/wiki/Q5133829","display_name":"Clinical decision support system","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C158846371","wikidata":"https://www.wikidata.org/wiki/Q1642137","display_name":"Blood flow","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist66504.2025.11268437","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268437","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","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":21,"referenced_works":["https://openalex.org/W1973952028","https://openalex.org/W1990093208","https://openalex.org/W2018504033","https://openalex.org/W2164526004","https://openalex.org/W2550180168","https://openalex.org/W2898314458","https://openalex.org/W3113216408","https://openalex.org/W3194478644","https://openalex.org/W3214581992","https://openalex.org/W4200111739","https://openalex.org/W4214920103","https://openalex.org/W4287891457","https://openalex.org/W4297458814","https://openalex.org/W4388933760","https://openalex.org/W4391138198","https://openalex.org/W4394950132","https://openalex.org/W4406845258","https://openalex.org/W4407062103","https://openalex.org/W4407736593","https://openalex.org/W4409172695","https://openalex.org/W4410793052"],"related_works":[],"abstract_inverted_index":{"Coronary":[0],"artery":[1],"disease":[2,217],"(CAD),":[3],"a":[4,25,158,164,171,193,208],"prevalent":[5],"cardiovascular":[6],"condition":[7],"caused":[8],"by":[9],"arterial":[10],"narrowing":[11],"or":[12],"blockage":[13],"that":[14],"restricts":[15],"blood":[16],"flow":[17],"to":[18,23,196],"the":[19,78,87,94,179],"heart":[20],"muscle,":[21],"continues":[22],"be":[24],"major":[26],"global":[27],"health":[28],"concern":[29],"with":[30,48,146,186],"vital":[31],"mortality":[32],"rates.":[33],"This":[34],"research":[35],"develops":[36],"an":[37],"intelligent":[38],"diagnostic":[39,202],"system":[40],"integrating":[41],"SERF":[42,184],"(Spin":[43],"Exchange":[44],"Relaxation":[45],"Free)":[46],"magnetocardiography":[47],"radiomics":[49,100,187],"analysis":[50],"for":[51,188,215],"early,":[52],"noninvasive":[53],"CAD":[54,66,190],"detection.":[55],"The":[56,204],"study":[57],"analyzed":[58],"cardiac":[59,212],"magnetic":[60,83],"signals":[61],"from":[62],"663":[63],"clinically":[64],"confirmed":[65],"cases,":[67],"stratified":[68],"into":[69],"severe":[70],"and":[71,81,90,118,140,170],"non-severe":[72],"stenosis":[73],"groups.":[74],"Using":[75],"0.9":[76],"times":[77],"maximum":[79],"positive":[80],"negative":[82],"field":[84],"intensities":[85],"of":[86,96,102,182],"ST":[88],"segment":[89],"T":[91],"wave":[92],"as":[93],"region":[95],"interest,":[97],"through":[98],"advanced":[99],"processing":[101],"2":[103],"D":[104],"temporal":[105],"isomagnetic":[106],"maps,":[107],"we":[108],"extracted":[109],"comprehensive":[110],"feature":[111],"sets":[112],"including":[113],"morphological":[114],"patterns,":[115],"first-order":[116],"statistics,":[117],"textural":[119],"characteristics.":[120],"Seven":[121],"machine":[122],"learning":[123],"models":[124],"algorithms":[125],"-":[126,142,156],"Logistic":[127],"Regression,":[128],"Support":[129],"Vector":[130],"Machine,":[131],"k-Nearest":[132],"Neighbors,":[133],"Naive":[134],"Bayes,":[135],"Decision":[136],"Tree,":[137],"Random":[138],"Forest,":[139],"XGBoost":[141],"were":[143],"systematically":[144],"evaluated,":[145],"experimental":[147],"results":[148],"showing":[149],"substantial":[150],"performance":[151],"gains":[152],"over":[153],"conventional":[154],"methods":[155],"achieving":[157],"$9.2":[159],"\\%$":[160,166],"improvement":[161],"in":[162,168,174,211],"accuracy,":[163],"$9":[165],"increase":[167],"F1-score,":[169],"0.118":[172],"rise":[173],"AUC.":[175],"These":[176],"findings":[177],"demonstrate":[178],"clinical":[180],"potential":[181],"combining":[183],"technology":[185],"enhanced":[189],"diagnosis,":[191],"offering":[192],"radiation-free":[194],"alternative":[195],"traditional":[197],"imaging":[198],"modalities":[199],"while":[200],"maintaining":[201],"reliability.":[203],"proposed":[205],"methodology":[206],"represents":[207],"significant":[209],"advancement":[210],"diagnostics,":[213],"particularly":[214],"early-stage":[216],"detection":[218],"where":[219],"timely":[220],"intervention":[221],"is":[222],"most":[223],"critical.":[224]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-03T00:00:00"}
