{"id":"https://openalex.org/W4415974363","doi":"https://doi.org/10.1016/j.procs.2025.09.624","title":"Image-to-Biomarker Prediction of CSF Amyloid-\u03b2 via Multimodal Deep Learning on MRI and Brain Perfusion SPECT","display_name":"Image-to-Biomarker Prediction of CSF Amyloid-\u03b2 via Multimodal Deep Learning on MRI and Brain Perfusion SPECT","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415974363","doi":"https://doi.org/10.1016/j.procs.2025.09.624"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2025.09.624","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.624","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2025.09.624","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Riku Hasegawa","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":false,"raw_author_name":"Riku Hasegawa","raw_affiliation_strings":["University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan"],"affiliations":[{"raw_affiliation_string":"University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan","institution_ids":["https://openalex.org/I180941496"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071501511","display_name":"Takashi Nakata","orcid":"https://orcid.org/0000-0002-6652-9447"},"institutions":[{"id":"https://openalex.org/I4210127032","display_name":"Himeji Medical Center","ror":"https://ror.org/037767x92","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210127032","https://openalex.org/I4210137409"]},{"id":"https://openalex.org/I916559398","display_name":"Kindai University","ror":"https://ror.org/05kt9ap64","country_code":"JP","type":"education","lineage":["https://openalex.org/I916559398"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takashi Nakata","raw_affiliation_strings":["Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo 670-8560, Japan","Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka 589-8511, Japan"],"affiliations":[{"raw_affiliation_string":"Hyogo Prefectural Harima-Himeji General Medical Center, 3-264 Kamiyacho, Himeji, Hyogo 670-8560, Japan","institution_ids":["https://openalex.org/I4210127032"]},{"raw_affiliation_string":"Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka 589-8511, Japan","institution_ids":["https://openalex.org/I916559398"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077973419","display_name":"Kazunari Ishii","orcid":"https://orcid.org/0000-0001-6601-952X"},"institutions":[{"id":"https://openalex.org/I916559398","display_name":"Kindai University","ror":"https://ror.org/05kt9ap64","country_code":"JP","type":"education","lineage":["https://openalex.org/I916559398"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazunari Ishii","raw_affiliation_strings":["Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka 589-8511, Japan"],"affiliations":[{"raw_affiliation_string":"Kindai University, 377-2 Ohnohigashi, Osakasayama, Osaka 589-8511, Japan","institution_ids":["https://openalex.org/I916559398"]}]},{"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":true,"raw_author_name":"Syoji Kobashi","raw_affiliation_strings":["University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan"],"affiliations":[{"raw_affiliation_string":"University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan","institution_ids":["https://openalex.org/I180941496"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042142624"],"corresponding_institution_ids":["https://openalex.org/I180941496"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37791318,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"270","issue":null,"first_page":"4978","last_page":"4987"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.7529000043869019,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.7529000043869019,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/T10137","display_name":"Multiple Sclerosis Research Studies","score":0.0357000008225441,"subfield":{"id":"https://openalex.org/subfields/2734","display_name":"Pathology and Forensic 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/T10086","display_name":"Alzheimer's disease research and treatments","score":0.025299999862909317,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"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/deep-learning","display_name":"Deep learning","score":0.7050999999046326},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6208000183105469},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4832000136375427},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.43309998512268066},{"id":"https://openalex.org/keywords/positron-emission-tomography","display_name":"Positron emission tomography","score":0.41749998927116394},{"id":"https://openalex.org/keywords/emission-computed-tomography","display_name":"Emission computed tomography","score":0.41350001096725464},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4122999906539917},{"id":"https://openalex.org/keywords/dementia","display_name":"Dementia","score":0.40959998965263367},{"id":"https://openalex.org/keywords/perfusion","display_name":"Perfusion","score":0.39820000529289246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7253999710083008},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7050999999046326},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6208000183105469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6014999747276306},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.43309998512268066},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C2778064278","wikidata":"https://www.wikidata.org/wiki/Q5372630","display_name":"Emission computed tomography","level":3,"score":0.41350001096725464},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4122999906539917},{"id":"https://openalex.org/C2779483572","wikidata":"https://www.wikidata.org/wiki/Q83030","display_name":"Dementia","level":3,"score":0.40959998965263367},{"id":"https://openalex.org/C146957229","wikidata":"https://www.wikidata.org/wiki/Q1266915","display_name":"Perfusion","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C135691158","wikidata":"https://www.wikidata.org/wiki/Q7168308","display_name":"Perfusion scanning","level":3,"score":0.39579999446868896},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3856000006198883},{"id":"https://openalex.org/C2779651940","wikidata":"https://www.wikidata.org/wiki/Q54196","display_name":"Cerebrospinal fluid","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3528999984264374},{"id":"https://openalex.org/C2780066322","wikidata":"https://www.wikidata.org/wiki/Q719444","display_name":"Lumbar puncture","level":3,"score":0.32519999146461487},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C154281038","wikidata":"https://www.wikidata.org/wiki/Q629232","display_name":"Cerebral perfusion pressure","level":3,"score":0.3059000074863434},{"id":"https://openalex.org/C2780640218","wikidata":"https://www.wikidata.org/wiki/Q8277","display_name":"Multiple sclerosis","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C44575665","wikidata":"https://www.wikidata.org/wiki/Q1293405","display_name":"Lumbar","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2780441642","wikidata":"https://www.wikidata.org/wiki/Q849737","display_name":"Single-photon emission computed tomography","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25920000672340393}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2025.09.624","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.624","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2025.09.624","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.624","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1972025839","https://openalex.org/W2168334242","https://openalex.org/W2309442902","https://openalex.org/W4310461604","https://openalex.org/W4404023882"],"related_works":[],"abstract_inverted_index":{"Alzheimer\u2019s":[0],"disease":[1],"(AD)":[2],"is":[3,58,78,86,180,192,214],"the":[4,23,101,188,271,277,298,309],"most":[5],"common":[6],"form":[7],"of":[8,25,50,103,136,232,259,279,300],"dementia.":[9,236],"Recently,":[10],"anti-amyloid-\u03b2":[11],"(A\u03b2)":[12,42],"antibody":[13],"therapy":[14],"has":[15,28,100],"been":[16],"applied":[17],"to":[18,55,64,81,182,198,208],"treat":[19],"early":[20,26,301],"AD.":[21],"Therefore,":[22],"diagnosis":[24],"AD":[27,51,83,302],"become":[29],"more":[30],"important":[31],"than":[32],"ever":[33],"before.":[34],"Cerebrospinal":[35],"fluid":[36],"(CSF)":[37],"biomarkers":[38],"such":[39],"as":[40,204,283],"amyloid-\u03b2":[41],"and":[43,60,69,107,152,166,187,224,246,268,286,307,312,317],"tau":[44],"proteins":[45],"are":[46,161],"widely":[47],"recognized":[48],"indicators":[49],"pathology.":[52],"Lumbar":[53],"puncture":[54],"obtain":[56],"CSF":[57,137,290],"invasive,":[59],"this":[61,124],"procedure":[62],"tends":[63],"be":[65,109],"unfavorable":[66],"for":[67,111,133,195,262,266,270,289],"patients":[68,234,316],"their":[70,318],"families.":[71,319],"Although":[72],"amyloid":[73],"positron":[74],"emission":[75,90],"tomography":[76,92],"(PET)":[77],"another":[79],"method":[80,145],"confirm":[82],"pathology,":[84],"it":[85],"expensive.":[87],"Single":[88],"photon":[89],"computed":[91],"(SPECT),":[93],"which":[94,191],"shows":[95],"regional":[96],"cerebral":[97],"blood":[98],"flow,":[99],"advantage":[102],"being":[104],"relatively":[105],"inexpensive":[106],"can":[108],"used":[110],"repeated":[112],"examination":[113],"under":[114],"medical":[115],"insurance":[116],"coverage":[117],"in":[118,296],"dementia":[119],"clinical":[120,304],"practice.":[121],"To":[122],"address":[123],"limitation,":[125],"we":[126],"present":[127],"an":[128,205],"image-to-biomarker":[129,281],"deep":[130],"learning":[131],"framework":[132],"non-invasive":[134],"prediction":[135,210,251],"A\u03b2":[138],"levels":[139],"by":[140],"leveraging":[141],"multimodal":[142],"neuroimaging.":[143],"Our":[144],"integrates":[146],"structural":[147],"information":[148,154,248],"from":[149,155],"T1-weighted":[150],"MRI":[151],"functional":[153],"brain":[156,243],"perfusion":[157,244],"SPECT.":[158],"All":[159],"images":[160],"spatially":[162],"normalized":[163],"using":[164,168,216],"DARTEL":[165],"processed":[167],"a":[169,217,230,284],"ResNet-based":[170],"3D":[171],"convolutional":[172],"neural":[173],"network":[174],"(CNN).":[175],"A":[176],"multitask":[177],"regression":[178],"model":[179,213],"employed":[181],"simultaneously":[183],"predict":[184],"A\u03b242,":[185,263],"A\u03b240,":[186,267],"A\u03b242/40":[189],"ratio,":[190],"clinically":[193],"valued":[194],"its":[196],"robustness":[197],"individual":[199],"variability,":[200],"with":[201,235],"age":[202,247],"incorporated":[203],"auxiliary":[206],"input":[207],"improve":[209],"performance.":[211],"The":[212],"trained":[215],"weighted":[218],"mean":[219,255],"squared":[220,256],"error":[221],"loss":[222],"function":[223],"evaluated":[225],"through":[226],"five-fold":[227],"cross-validation":[228],"on":[229,315],"dataset":[231],"153":[233],"Ablation":[237],"studies":[238],"demonstrate":[239],"that":[240],"including":[241],"both":[242],"SPECT":[245],"significantly":[249],"improves":[250],"performance,":[252],"achieving":[253],"root":[254],"errors":[257],"(RMSEs)":[258],"500":[260],"pg/mL":[261,265],"3,838":[264],"0.0355":[269],"A\u03b242/A\u03b240":[272],"ratio.":[273],"These":[274],"findings":[275],"underscore":[276],"potential":[278],"our":[280],"approach":[282],"practical":[285],"accurate":[287],"solution":[288],"biomarker":[291],"prediction,":[292],"offering":[293],"promising":[294],"applications":[295],"improving":[297],"accuracy":[299],"diagnosis,":[303],"decision-making":[305],"support,":[306],"reducing":[308],"psychological,":[310],"physical,":[311],"financial":[313],"burden":[314]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-11-06T00:00:00"}