{"id":"https://openalex.org/W4409357046","doi":"https://doi.org/10.1109/jbhi.2025.3557065","title":"Accurate Multi-Landmark Localization in 3D Ultra-High Resolution CT Images of the Ears Via Deep Reinforcement Learning and Transformer","display_name":"Accurate Multi-Landmark Localization in 3D Ultra-High Resolution CT Images of the Ears Via Deep Reinforcement Learning and Transformer","publication_year":2025,"publication_date":"2025-04-11","ids":{"openalex":"https://openalex.org/W4409357046","doi":"https://doi.org/10.1109/jbhi.2025.3557065","pmid":"https://pubmed.ncbi.nlm.nih.gov/40215147"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2025.3557065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2025.3557065","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5054250124","display_name":"Zhiwei Qu","orcid":"https://orcid.org/0000-0001-9886-914X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiwei Qu","raw_affiliation_strings":["School of Information Science and Technology, Beiing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9886-914X","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Beiing University of Technology, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Li Zhuo","orcid":"https://orcid.org/0000-0002-9937-2669"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li Zhuo","raw_affiliation_strings":["School of Information Science and Technology, Beiing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9937-2669","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Beiing University of Technology, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068584522","display_name":"Ning Xu","orcid":"https://orcid.org/0000-0001-7909-7025"},"institutions":[{"id":"https://openalex.org/I183519381","display_name":"Capital Medical University","ror":"https://ror.org/013xs5b60","country_code":"CN","type":"education","lineage":["https://openalex.org/I183519381"]},{"id":"https://openalex.org/I4210147433","display_name":"Beijing Friendship Hospital","ror":"https://ror.org/053qy4437","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210147433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Xu","raw_affiliation_strings":["Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China","institution_ids":["https://openalex.org/I4210147433","https://openalex.org/I183519381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100609960","display_name":"Hongxia Yin","orcid":"https://orcid.org/0000-0003-2804-1253"},"institutions":[{"id":"https://openalex.org/I183519381","display_name":"Capital Medical University","ror":"https://ror.org/013xs5b60","country_code":"CN","type":"education","lineage":["https://openalex.org/I183519381"]},{"id":"https://openalex.org/I4210147433","display_name":"Beijing Friendship Hospital","ror":"https://ror.org/053qy4437","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210147433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxia Yin","raw_affiliation_strings":["Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2804-1253","affiliations":[{"raw_affiliation_string":"Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China","institution_ids":["https://openalex.org/I4210147433","https://openalex.org/I183519381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112992039","display_name":"Zhenchang Wang","orcid":"https://orcid.org/0000-0001-8190-6469"},"institutions":[{"id":"https://openalex.org/I183519381","display_name":"Capital Medical University","ror":"https://ror.org/013xs5b60","country_code":"CN","type":"education","lineage":["https://openalex.org/I183519381"]},{"id":"https://openalex.org/I4210147433","display_name":"Beijing Friendship Hospital","ror":"https://ror.org/053qy4437","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210147433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenchang Wang","raw_affiliation_strings":["Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8190-6469","affiliations":[{"raw_affiliation_string":"Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China","institution_ids":["https://openalex.org/I4210147433","https://openalex.org/I183519381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100373866","display_name":"Xiaoguang Li","orcid":"https://orcid.org/0000-0002-7307-6263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaoguang Li","raw_affiliation_strings":["School of Information Science and Technology, Beiing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7307-6263","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Beiing University of Technology, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1388,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.84515828,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"29","issue":"8","first_page":"5787","last_page":"5800"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.9437999725341797,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.9437999725341797,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.858644962310791},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7099508047103882},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6419234275817871},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6164655685424805},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5120804905891418},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5060133337974548},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.45990630984306335},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4523230493068695},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.44896626472473145},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.4412012994289398},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3970154821872711},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21764573454856873},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17896106839179993},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.15482261776924133},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.15042400360107422},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14831283688545227},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.14804741740226746},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.10789152979850769},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.08005303144454956}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.858644962310791},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7099508047103882},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6419234275817871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6164655685424805},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5120804905891418},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5060133337974548},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.45990630984306335},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4523230493068695},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.44896626472473145},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.4412012994289398},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3970154821872711},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21764573454856873},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17896106839179993},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.15482261776924133},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.15042400360107422},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14831283688545227},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.14804741740226746},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.10789152979850769},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.08005303144454956}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098408","descriptor_name":"Reinforcement Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098408","descriptor_name":"Reinforcement Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098408","descriptor_name":"Reinforcement Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004423","descriptor_name":"Ear","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D004423","descriptor_name":"Ear","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D004423","descriptor_name":"Ear","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D059925","descriptor_name":"Anatomic Landmarks","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D059925","descriptor_name":"Anatomic Landmarks","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"descriptor_ui":"D059925","descriptor_name":"Anatomic Landmarks","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2025.3557065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2025.3557065","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:40215147","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40215147","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":"IEEE journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1464635397","display_name":null,"funder_award_id":"62371316","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2862667910","display_name":null,"funder_award_id":"62472013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5108751638","display_name":null,"funder_award_id":"62276012","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5496908124","display_name":null,"funder_award_id":"62431001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1984711392","https://openalex.org/W1995003188","https://openalex.org/W2275865840","https://openalex.org/W2526653212","https://openalex.org/W2569670712","https://openalex.org/W2746553466","https://openalex.org/W2765894129","https://openalex.org/W2771590529","https://openalex.org/W2903819949","https://openalex.org/W2925288829","https://openalex.org/W2962611724","https://openalex.org/W2979352690","https://openalex.org/W2979784156","https://openalex.org/W3035565332","https://openalex.org/W3041267126","https://openalex.org/W3094502228","https://openalex.org/W3098592896","https://openalex.org/W3126345029","https://openalex.org/W3142022860","https://openalex.org/W3165043602","https://openalex.org/W3183452672","https://openalex.org/W3214121008","https://openalex.org/W4200597473","https://openalex.org/W4221104574","https://openalex.org/W4225370003","https://openalex.org/W4280586676","https://openalex.org/W4309437573","https://openalex.org/W4313020741","https://openalex.org/W4317377133","https://openalex.org/W4323357155","https://openalex.org/W4376273098","https://openalex.org/W4381855672","https://openalex.org/W4385245566","https://openalex.org/W4386275863","https://openalex.org/W4386597894","https://openalex.org/W4387415072","https://openalex.org/W6685444567"],"related_works":["https://openalex.org/W2056853153","https://openalex.org/W2057559274","https://openalex.org/W2026924879","https://openalex.org/W2005087563","https://openalex.org/W2378111931","https://openalex.org/W2052388267","https://openalex.org/W2950647290","https://openalex.org/W2620829895","https://openalex.org/W2356918560","https://openalex.org/W4243161226"],"abstract_inverted_index":{"Automated":[0],"landmark":[1,55,199],"localization":[2,56,64,72,89,154,179,243,249],"can":[3,151,175,222],"help":[4],"radiologists":[5],"quickly":[6,106],"determine":[7],"the":[8,35,39,100,117,131,135,138,143,148,177,185,194,204,211,219,233,240],"locations":[9],"of":[10,134,141,155,181,198,227,236],"key":[11],"structures":[12,123],"or":[13],"lesion":[14],"areas":[15],"from":[16,124],"medical":[17,24,59],"images.":[18,161],"However,":[19],"when":[20],"facing":[21],"large-volume":[22],"3D":[23,58,76,159,207],"images,":[25],"existing":[26,50],"methods":[27,51,244],"have":[28],"very":[29],"high":[30],"computational":[31],"complexity":[32],"due":[33],"to":[34,37,44,52,105,115,129,183],"need":[36],"encode":[38],"global":[40,144],"image.":[41],"That":[42],"is":[43,47,82,103,113,146],"say,":[45],"it":[46],"difficult":[48],"for":[49,74],"achieve":[53,152,223],"accurate":[54,70],"in":[57,158],"images":[60,81],"at":[61],"a":[62,87,165,247],"faster":[63,248],"speed.":[65,250],"In":[66],"this":[67],"paper,":[68],"an":[69],"multi-landmark":[71,157,182,242],"method":[73,85,150,221],"ear":[75,156,206],"Ultra-High":[77],"Resolution":[78],"CT":[79],"(U-HRCT)":[80],"proposed.":[83],"This":[84],"adopts":[86],"novel":[88],"pipeline":[90],"that":[91],"combines":[92],"Deep":[93],"Reinforcement":[94],"Learning":[95],"(DRL)":[96],"and":[97,196,210,229,245],"Transformer.":[98],"Firstly,":[99],"DRL":[101],"algorithm":[102],"used":[104,114],"collect":[107],"landmark-related":[108],"local":[109,127],"features.":[110],"Secondly,":[111],"Transformer":[112],"extract":[116],"spatial":[118,186],"position":[119,133,187],"relationship":[120,188],"between":[121,189],"anatomical":[122],"these":[125],"discrete":[126],"features":[128],"infer":[130],"coordinate":[132],"landmark.":[136],"Because":[137],"complex":[139],"process":[140],"encoding":[142],"image":[145],"avoided,":[147],"proposed":[149,164,220],"fast":[153,178],"U-HRCT":[160,208],"Finally,":[162],"we":[163],"refinement":[166],"module":[167],"based":[168],"on":[169,203],"dual-branch":[170],"hybrid":[171],"Multi-Layer":[172],"Perceptron,":[173],"which":[174],"use":[176],"results":[180,202],"learn":[184],"landmarks,":[190],"thereby":[191],"further":[192],"improving":[193],"accuracy":[195],"stability":[197],"localization.":[200],"Experimental":[201],"self-built":[205],"dataset":[209,216],"publicly":[212],"available":[213],"2D":[214],"cephalometric":[215],"demonstrate":[217],"that,":[218],"Successful":[224],"Detection":[225],"Rate":[226],"96.71%":[228],"89.97%":[230],"respectively":[231],"within":[232],"precision":[234],"range":[235],"2.0":[237],"mm,":[238],"surpassing":[239],"state-of-the-art":[241],"has":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
