{"id":"https://openalex.org/W4405602460","doi":"https://doi.org/10.3389/fncom.2024.1523973","title":"Editorial: Deep learning and neuroimage processing in understanding neurological diseases","display_name":"Editorial: Deep learning and neuroimage processing in understanding neurological diseases","publication_year":2024,"publication_date":"2024-12-19","ids":{"openalex":"https://openalex.org/W4405602460","doi":"https://doi.org/10.3389/fncom.2024.1523973","pmid":"https://pubmed.ncbi.nlm.nih.gov/39749286"},"language":"en","primary_location":{"id":"doi:10.3389/fncom.2024.1523973","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2024.1523973","pdf_url":"https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1523973/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},"type":"editorial","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1523973/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006393368","display_name":"Joana Carvalho","orcid":"https://orcid.org/0000-0002-0081-1976"},"institutions":[{"id":"https://openalex.org/I113358080","display_name":"Champalimaud Foundation","ror":"https://ror.org/03g001n57","country_code":"PT","type":"facility","lineage":["https://openalex.org/I113358080"]},{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Joana Carvalho","raw_affiliation_strings":["Laboratory of Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal","Laboratory of Visual Neuroscience, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory of Preclinical MRI, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal","institution_ids":["https://openalex.org/I113358080"]},{"raw_affiliation_string":"Laboratory of Visual Neuroscience, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal","institution_ids":["https://openalex.org/I76903346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035817669","display_name":"Ali Abdollahzadeh","orcid":"https://orcid.org/0000-0001-6234-2012"},"institutions":[{"id":"https://openalex.org/I175532246","display_name":"University of Eastern Finland","ror":"https://ror.org/00cyydd11","country_code":"FI","type":"education","lineage":["https://openalex.org/I175532246"]},{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["FI","US"],"is_corresponding":false,"raw_author_name":"Ali Abdollahzadeh","raw_affiliation_strings":["A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland","Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland","institution_ids":["https://openalex.org/I175532246"]},{"raw_affiliation_string":"Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027699113","display_name":"Ricardo J. Ferrari","orcid":"https://orcid.org/0000-0003-1197-2553"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ricardo Jos\u00e9 Ferrari","raw_affiliation_strings":["Biomedical Image Processing (BIP) Lab, Department of Computing, Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Image Processing (BIP) Lab, Department of Computing, Federal University of S\u00e3o Carlos, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I177909021"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5006393368"],"corresponding_institution_ids":["https://openalex.org/I113358080","https://openalex.org/I76903346"],"apc_list":{"value":3295,"currency":"USD","value_usd":3295},"apc_paid":{"value":3295,"currency":"USD","value_usd":3295},"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":null,"first_page":"1523973","last_page":"1523973"},"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":0.9955000281333923,"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":0.9955000281333923,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9950000047683716,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/neuroimaging","display_name":"Neuroimaging","score":0.8307450413703918},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.616986095905304},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5873376131057739},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5626367330551147},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5470082759857178},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.51772540807724},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4313172698020935},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41832953691482544},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.3260844945907593},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2882145047187805},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.17774084210395813},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.1462276577949524}],"concepts":[{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.8307450413703918},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.616986095905304},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5873376131057739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5626367330551147},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5470082759857178},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.51772540807724},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4313172698020935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41832953691482544},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.3260844945907593},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2882145047187805},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.17774084210395813},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.1462276577949524},{"id":"https://openalex.org/C138496976","wikidata":"https://www.wikidata.org/wiki/Q175002","display_name":"Developmental psychology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3389/fncom.2024.1523973","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2024.1523973","pdf_url":"https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1523973/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},{"id":"pmid:39749286","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39749286","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in computational neuroscience","raw_type":null},{"id":"pmh:oai:estudogeral.uc.pt:10316/123016","is_oa":true,"landing_page_url":"https://hdl.handle.net/10316/123016","pdf_url":"https://estudogeral.uc.pt/bitstream/10316/123016/1/Editorial%20Deep%20learning%20and%20neuroimage%20processing%20in%20understanding%20neurological%20diseases.pdf","source":{"id":"https://openalex.org/S4306402070","display_name":"Estudo Geral (Universidad de Coimbra)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I76903346","host_organization_name":"University of Coimbra","host_organization_lineage":["https://openalex.org/I76903346"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pubmedcentral.nih.gov:11693727","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11693727","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11693727/pdf/fncom-18-1523973.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"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":"Front Comput Neurosci","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:34e7f3a9c0204e82be43f179d33a90e2","is_oa":true,"landing_page_url":"https://doaj.org/article/34e7f3a9c0204e82be43f179d33a90e2","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":"Frontiers in Computational Neuroscience, Vol 18 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fncom.2024.1523973","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fncom.2024.1523973","pdf_url":"https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1523973/pdf","source":{"id":"https://openalex.org/S19778766","display_name":"Frontiers in Computational Neuroscience","issn_l":"1662-5188","issn":["1662-5188"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computational Neuroscience","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4405602460.pdf"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W3001152983","https://openalex.org/W3004868960","https://openalex.org/W3014974815","https://openalex.org/W3023079706","https://openalex.org/W3028393291","https://openalex.org/W3112701542","https://openalex.org/W3172681723","https://openalex.org/W4391109864"],"related_works":["https://openalex.org/W2118717649","https://openalex.org/W2413243053","https://openalex.org/W410723623","https://openalex.org/W2015341305","https://openalex.org/W2035068594","https://openalex.org/W3024479225","https://openalex.org/W3171371563","https://openalex.org/W4367060753","https://openalex.org/W4379141755","https://openalex.org/W3003847115"],"abstract_inverted_index":{"Recent":[0],"advancements":[1,76],"in":[2,7,26,77,82,99,122,147,282,366,382,502,535,614,618,644,653,700,773,786,846],"neuroimaging":[3,48,78,123,141,570,664],"have":[4,778],"been":[5,307,780],"instrumental":[6],"improving":[8,126],"the":[9,119,184,205,239,367,371,456,568,625,631,634,677,752,768,905],"diagnosis,":[10,152,537],"treatment":[11,148,541,914],"planning,":[12,151],"and":[13,35,51,58,94,104,113,129,149,153,163,166,195,202,222,232,237,315,377,389,406,424,484,490,510,521,539,579,636,661,663,713,721,744,757,793,805,867,879,892,897,910],"monitoring":[14,154],"of":[15,96,140,169,242,339,374,403,464,479,546,552,627,638,668,680,771,836,870,907],"neurological":[16,871],"diseases":[17],"(Qiu":[18],"et":[19,65,156,246,268,359,397,527,563,591,689],"al.":[20,66,157,247,269,398,528,564,592,690],"2020).":[21,216,529,565],"DL-powered":[22],"algorithms":[23],"now":[24],"excel":[25],"tasks":[27,534],"like":[28,47,873],"brain":[29,83,375,401,657,748,787,816,837,877],"segmentation,":[30,864],"lesion":[31],"detection,":[32,640],"cohort":[33,87,500],"classification,":[34],"biomarker":[36,865],"discovery,":[37],"offering":[38,331,822,919],"unprecedented":[39],"insights":[40,824],"into":[41,825],"high-resolution":[42],"imaging":[43,548],"data.":[44,413,450,789],"However,":[45,249],"challenges":[46,583,652],"data":[49,101,142,258,298,352,365,429,557,665,737],"variability":[50,121,199],"quality,":[52],"limited":[53,678],"training":[54,257,368,457],"on":[55,320,351,400,418,436,441,448,476,601,731,889],"pathological":[56,354],"cases,":[57,443],"poor":[59],"generalizability":[60,338,735],"across":[61,543],"modalities":[62],"persist":[63],"(Ma":[64],"2024).This":[67],"special":[68],"issue":[69],"addresses":[70],"these":[71,219,686],"challenges,":[72],"presenting":[73],"recent":[74],"DL":[75,251,340,480,505,553,647,693,772,776,860],"analysis,":[79,630],"including":[80,363],"enhancements":[81],"segmentation":[84,139,160,182,225,254,344,373,432,896],"accuracy,":[85,745],"refined":[86],"classification":[88,501,598,620],"strategies,":[89],"improved":[90,347,370,431],"anatomical":[91,285,827],"feature":[92,711],"extraction,":[93],"investigations":[95],"gender":[97,801],"differences":[98,785],"Neuroimaging":[100],"(CT,":[102],"MRI":[103,283,725,788],"EEG).":[105],"The":[106,288,323,414,739,883],"goal":[107],"is":[108,188,196,554,567,659,666],"to":[109,134,198,277,309,459,472,481,558,672,695,717,782,799,813,819,843,851,903,922],"develop":[110],"more":[111,894,912],"robust":[112,333],"generalizable":[114],"models":[115,252,318,341,506,884],"that":[116,362,420,454,516],"can":[117,345,507],"accommodate":[118],"inherent":[120],"datasets,":[124,733],"ultimately":[125,849],"clinical":[127,764],"decision-making":[128],"patient":[130,924],"outcomes.Deep":[131],"learning":[132,274,317],"methods":[133,226,314,471],"improve":[135,467,885,923],"biomedical":[136,503],"image":[137,807],"segmentationThe":[138],"plays":[143],"a":[144,271,292,332,393,461,496,544,692,704,715],"crucial":[145],"role":[146,643,770],"surgical":[150],"(Isensee":[155],"2021).":[158],"Image":[159],"involves":[161],"identifying":[162],"outlining":[164],"structural":[165],"functional":[167],"regions":[168,817],"interest":[170],"(ROIs),":[171],"lesions,":[172,878],"vessels,":[173],"or":[174],"white":[175,279,758],"matter":[176,280],"tracts,":[177],"among":[178],"others.":[179],"Although":[180],"manual":[181,262],"remains":[183],"ground":[185],"truth,":[186],"it":[187,409,495],"highly":[189],"time-consuming,":[190],"labor-intensive,":[191],"requires":[192],"expert":[193],"knowledge,":[194],"prone":[197],"between":[200,518,719],"observers":[201],"even":[203],"within":[204],"same":[206],"observer":[207],"over":[208],"time":[209,231],"(":[210],"Haque":[211],"I":[212],"&amp;":[213],"Neubert":[214],"J,":[215],"To":[217,264,587,684],"address":[218,265,588,685],"limitations,":[220],"semi-automatic":[221],"fully":[223,707],"automatic":[224,343,372],"offer":[227],"significant":[228,619,920],"advantages,":[229],"reducing":[230],"labor":[233],"requirements,":[234],"enhancing":[235,641,862],"consistency,":[236],"enabling":[238],"efficient":[240],"analysis":[241],"large-scale":[243],"datasets":[244,419,492],"(Antonelli":[245],"2022).":[248],"most":[250],"for":[253,335,342,499,572,584,710,763,916],"require":[255],"labeled":[256],"obtained":[259],"through":[260,736],"experts\u2019":[261],"annotation.":[263],"this,":[266,589],"Duarte":[267],"introduced":[270,593],"multi-stage":[272],"semi-supervised":[273,336],"(M3SL)":[275],"approach":[276,305,728],"segment":[278],"hyperintensities":[281],"T1-weighted":[284],"scans":[286,402],"automatically.":[287],"M3SL":[289,324],"method":[290,325],"employs":[291],"three-step":[293],"optimization":[294],"process,":[295],"leveraging":[296],"unannotated":[297],"segmented":[299],"by":[300,348,676],"traditional":[301],"processing":[302],"methods.":[303],"This":[304,451,530,608,727,766,829,855],"has":[306,493],"shown":[308],"outperform":[310],"both":[311],"conventional":[312],"baseline":[313],"deep":[316],"based":[319,475,600],"transfer":[321],"learning.":[322],"demonstrates":[326],"resilience":[327],"against":[328],"annotation":[329],"scarcity,":[330],"alternative":[334],"segmentation.The":[337],"be":[346],"retraining":[349],"them":[350],"from":[353,488],"cases.":[355,426],"For":[356,790],"instance,":[357],"Gerken":[358,388],"al,":[360],"demonstrated":[361],"hemorrhage":[364,412,428,437],"set":[369,458],"parenchyma":[376],"cerebrospinal":[378],"fluid,":[379],"i.e.,":[380],"ventricles,":[381],"CT":[383],"images.":[384],"In":[385],"this":[386],"study,":[387],"colleagues":[390,794],"first":[391],"trained":[392,714],"2D":[394],"U-Net":[395],"(Ronneberger":[396],"2015)":[399],"healthy":[404,519],"participants":[405],"subsequently":[407],"retrained":[408],"with":[410,523,746,802],"additional":[411],"model":[415,599,609,694],"was":[416],"tested":[417],"included":[421],"normal,":[422],"hemorrhage,":[423],"tumor":[425,442],"Adding":[427],"significantly":[430],"performance,":[433],"not":[434,831],"only":[435,832],"cases":[438],"but":[439,839],"also":[440,649,779,840],"without":[444],"negatively":[445],"impacting":[446],"performance":[447,621],"normal":[449],"study":[452],"shows":[453],"expanding":[455],"include":[460],"broader":[462],"range":[463,545],"pathologies":[465],"may":[466],"generalizability.":[468],"Deep":[469],"Learning":[470],"categorize":[473],"cohorts":[474],"neuroimagingThe":[477],"capacity":[478],"automatically":[482],"extract":[483],"learn":[485],"complex":[486],"patterns":[487],"vast":[489],"diverse":[491],"made":[494],"transformative":[497],"tool":[498],"research.":[504],"accurately":[508],"analyze":[509],"classify":[511,800],"images,":[512],"detecting":[513],"subtle":[514],"features":[515],"distinguish":[517],"individuals":[520],"those":[522],"specific":[524],"conditions":[525,872],"(Chan":[526],"capability":[531],"enables":[532],"critical":[533],"disease":[536],"prognosis,":[538],"predicting":[540],"response":[542],"medical":[547],"modalities.One":[549],"prominent":[550],"application":[551],"analyzing":[555],"EEG":[556,566,615,628],"detect":[559,696],"epileptic":[560,596],"seizures":[561],"(Gao":[562],"preferred":[569],"modality":[571],"seizure":[573,597,639],"detection;":[574],"however,":[575],"its":[576],"strong":[577],"spatial":[578],"temporal":[580],"correlations":[581],"pose":[582],"accurate":[585,895],"classification.":[586],"Hu":[590],"an":[594],"innovative":[595,859],"Iterative":[602],"Gated":[603],"Graph":[604],"Convolutional":[605],"Networks":[606],"(IGGCN).":[607],"effectively":[610],"captures":[611],"long-term":[612],"dependencies":[613],"data,":[616,891],"resulting":[617],"improvements.":[622],"By":[623],"addressing":[624],"complexities":[626],"signal":[629],"IGGCN":[632],"advances":[633],"accuracy":[635,804],"reliability":[637],"DL\u2019s":[642],"neuroimaging-based":[645],"diagnostics.Similarly,":[646],"techniques":[648,777],"encounter":[650],"unique":[651],"pediatric":[654,774],"neuroimaging.":[655],"Pediatric":[656],"development":[658,906],"rapid":[660],"dynamic,":[662],"often":[667],"lower":[669],"quality":[670],"due":[671],"motion":[673],"artifacts,":[674],"compounded":[675],"availability":[679],"age-specific,":[681],"high-quality":[682],"datasets.":[683],"obstacles,":[687],"Das":[688],"used":[691,795],"prenatal":[697,880],"alcohol":[698,881],"exposure":[699],"children.":[701],"They":[702],"applied":[703,781],"pre-trained":[705],"simple":[706],"convolutional":[708,796],"network":[709],"extraction":[712],"classifier":[716,740],"differentiate":[718],"exposed":[720],"unexposed":[722],"children":[723],"using":[724],"scans.":[726],"minimized":[729],"reliance":[730,888],"large":[732],"boosting":[734],"augmentation.":[738],"achieved":[741],"high":[742,803],"sensitivity":[743],"key":[747,815],"regions,":[749],"such":[750],"as":[751,761],"corpus":[753],"callosum,":[754],"cerebellum,":[755],"pons,":[756],"matter,":[759],"identified":[760],"predictive":[762],"decision-making.":[765],"underscores":[767],"valuable":[769,823],"imaging.":[775],"investigate":[783],"sex":[784,820],"example,":[791],"Dibaji":[792],"neural":[797],"networks":[798],"minimal":[806],"preprocessing.":[808],"Saliency":[809],"maps":[810],"were":[811],"employed":[812],"identify":[814],"contributing":[818,850],"differentiation,":[821],"sex-specific":[826],"differences.":[828],"research":[830],"improves":[833],"our":[834],"understanding":[835],"structure":[838],"suggests":[841],"strategies":[842],"mitigate":[844],"bias":[845],"AI":[847],"models,":[848],"fairer":[852],"healthcare":[853],"outcomes.Conclusion:":[854],"Research":[856],"Topic":[857],"highlights":[858],"approaches":[861],"tissue":[863],"identification,":[866],"early":[868],"diagnosis":[869],"epilepsy,":[874],"cognitive":[875],"impairment,":[876],"exposure.":[882],"generalizability,":[886],"reduce":[887],"annotated":[890],"deliver":[893],"pathology":[898],"detection.":[899],"These":[900],"findings":[901],"promise":[902],"accelerate":[904],"diagnostic":[908],"tools":[909],"enable":[911],"personalized":[913],"plans":[915],"neurologic":[917],"conditions,":[918],"potential":[921],"outcomes.":[925]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
