{"id":"https://openalex.org/W7151601051","doi":"https://doi.org/10.1109/icmla66185.2025.00149","title":"Deep Learning for Third Molar Segmentation: An Empirical Analysis on Dental Radiographs","display_name":"Deep Learning for Third Molar Segmentation: An Empirical Analysis on Dental Radiographs","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151601051","doi":"https://doi.org/10.1109/icmla66185.2025.00149"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/A5113350644","display_name":"Munim Ahmed Ahmed","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129013","display_name":"Hospital Riotinto","ror":"https://ror.org/038152964","country_code":"ES","type":"healthcare","lineage":["https://openalex.org/I158283456","https://openalex.org/I4210129013"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Munim Ahmed","raw_affiliation_strings":["RIoT Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIoT Research Center","institution_ids":["https://openalex.org/I4210129013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121488410","display_name":"M. M. Mahbubul Syeed","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129013","display_name":"Hospital Riotinto","ror":"https://ror.org/038152964","country_code":"ES","type":"healthcare","lineage":["https://openalex.org/I158283456","https://openalex.org/I4210129013"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"M. M. Mahbubul Syeed","raw_affiliation_strings":["RIoT Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIoT Research Center","institution_ids":["https://openalex.org/I4210129013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079537280","display_name":"Jahid Hasan Rony","orcid":"https://orcid.org/0000-0002-6492-745X"},"institutions":[{"id":"https://openalex.org/I4210129013","display_name":"Hospital Riotinto","ror":"https://ror.org/038152964","country_code":"ES","type":"healthcare","lineage":["https://openalex.org/I158283456","https://openalex.org/I4210129013"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jahid Hasan Rony","raw_affiliation_strings":["RIoT Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIoT Research Center","institution_ids":["https://openalex.org/I4210129013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006031820","display_name":"Razib Hayat Khan","orcid":"https://orcid.org/0000-0001-6718-9918"},"institutions":[{"id":"https://openalex.org/I4210129013","display_name":"Hospital Riotinto","ror":"https://ror.org/038152964","country_code":"ES","type":"healthcare","lineage":["https://openalex.org/I158283456","https://openalex.org/I4210129013"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Razib Hayat Khan","raw_affiliation_strings":["RIoT Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIoT Research Center","institution_ids":["https://openalex.org/I4210129013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037927929","display_name":"Mohammad Faisal Uddin","orcid":"https://orcid.org/0000-0002-1875-7146"},"institutions":[{"id":"https://openalex.org/I4210129013","display_name":"Hospital Riotinto","ror":"https://ror.org/038152964","country_code":"ES","type":"healthcare","lineage":["https://openalex.org/I158283456","https://openalex.org/I4210129013"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Mohammad Faisal Uddin","raw_affiliation_strings":["RIoT Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIoT Research Center","institution_ids":["https://openalex.org/I4210129013"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050509391","display_name":"Safiqul Islam","orcid":"https://orcid.org/0000-0003-4683-8792"},"institutions":[{"id":"https://openalex.org/I184531372","display_name":"OsloMet \u2013 Oslo Metropolitan University","ror":"https://ror.org/04q12yn84","country_code":"NO","type":"education","lineage":["https://openalex.org/I184531372"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Safiqul Islam","raw_affiliation_strings":["Oslo Metropolitan University,Norway"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oslo Metropolitan University,Norway","institution_ids":["https://openalex.org/I184531372"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133125345","display_name":"Mohammad Khursheed Alam","orcid":null},"institutions":[{"id":"https://openalex.org/I199702508","display_name":"Jouf University","ror":"https://ror.org/02zsyt821","country_code":"SA","type":"education","lineage":["https://openalex.org/I199702508"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Mohammad Khursheed Alam","raw_affiliation_strings":["Jouf University,Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jouf University,Saudi Arabia","institution_ids":["https://openalex.org/I199702508"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5113350644"],"corresponding_institution_ids":["https://openalex.org/I4210129013"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.759381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"990","last_page":"995"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.9628000259399414,"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":0.9628000259399414,"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"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.0035000001080334187,"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/T12439","display_name":"Dental Research and COVID-19","score":0.0034000000450760126,"subfield":{"id":"https://openalex.org/subfields/3500","display_name":"General Dentistry"},"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/radiography","display_name":"Radiography","score":0.5205000042915344},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46459999680519104},{"id":"https://openalex.org/keywords/molar","display_name":"Molar","score":0.4546999931335449}],"concepts":[{"id":"https://openalex.org/C29694066","wikidata":"https://www.wikidata.org/wiki/Q118301","display_name":"Orthodontics","level":1,"score":0.5522000193595886},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.5205000042915344},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4878999888896942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4740999937057495},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C202271784","wikidata":"https://www.wikidata.org/wiki/Q189218","display_name":"Molar","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C199343813","wikidata":"https://www.wikidata.org/wiki/Q12128","display_name":"Dentistry","level":1,"score":0.37880000472068787},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.28700000047683716},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2558000087738037},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24650000035762787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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":13,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2969640942","https://openalex.org/W3035951809","https://openalex.org/W3170841864","https://openalex.org/W3211490618","https://openalex.org/W4220870608","https://openalex.org/W4312815172","https://openalex.org/W4388145582","https://openalex.org/W4403014858","https://openalex.org/W4405284189","https://openalex.org/W4408252736","https://openalex.org/W4409054257","https://openalex.org/W4409071415"],"related_works":[],"abstract_inverted_index":{"Third":[0],"molar":[1,69],"segmentation":[2,40,128],"on":[3,72],"panoramic":[4,63],"dental":[5,176,196],"radiographs":[6],"is":[7],"crucial":[8],"for":[9],"oral":[10],"diagnosis":[11],"and":[12,27,52,89,96,119,148,162,171,199,206],"effective":[13],"treatment":[14],"planning.":[15],"However,":[16],"it":[17],"remains":[18],"a":[19,34,57,99],"challenging":[20],"task":[21],"due":[22],"to":[23,195],"anatomical":[24,173],"complexity,":[25],"variability,":[26],"frequent":[28],"image":[29,197],"artifacts.":[30],"This":[31],"work":[32],"presents":[33],"comprehensive":[35],"benchmarking":[36],"of":[37,60,146,153,180,189,203],"state-ofthe-art":[38],"semantic":[39],"models":[41,93,158],"from":[42],"three":[43],"architecture":[44],"families:":[45],"Convolutional":[46],"Neural":[47],"Networks":[48],"(CNNs),":[49],"Transformer-based":[50],"models,":[51],"Hybrid":[53],"architectures.":[54],"We":[55],"introduce":[56],"novel":[58],"dataset":[59,207],"871":[61],"annotated":[62],"radiographs,":[64],"labeled":[65],"with":[66,103,123],"four":[67],"third":[68],"classes":[70],"based":[71],"the":[73,126,142,149,187,201],"FDI":[74],"numbering":[75],"system.":[76],"The":[77,178],"study":[78,182],"evaluates":[79],"U-Net":[80,122],"(with":[81],"MobileNetV2,":[82],"ResNet34,":[83],"ResNet50),":[84],"DeepLabV3,":[85],"SegFormer,":[86],"SETR,":[87],"DETR,":[88],"Mask2Former":[90,163],"models.":[91],"All":[92],"were":[94],"trained":[95],"compared":[97],"under":[98],"consistent":[100],"experimental":[101],"setup":[102],"extensive":[104],"hyperparameter":[105],"tuning.":[106],"Performance":[107],"was":[108],"assessed":[109],"using":[110],"multiple":[111],"metrics:":[112],"Mean":[113],"IoU,":[114],"Dice":[115],"Score,":[116],"Precision,":[117],"Recall,":[118],"Pixel":[120],"Accuracy.":[121],"ResNet34":[124],"achieved":[125],"highest":[127],"performance":[129],"(Mean":[130],"IoU:":[131],"0.7771,":[132],"Dice:":[133],"0.8691),":[134],"closely":[135],"followed":[136],"by":[137],"SegFormer.":[138],"These":[139],"results":[140],"highlight":[141],"superior":[143],"spatial":[144],"localization":[145],"CNNs":[147],"contextual":[150],"modeling":[151],"strength":[152],"Transformers.":[154],"In":[155],"contrast,":[156],"hybrid":[157],"such":[159],"as":[160],"DETR":[161],"ranked":[164],"lower,":[165],"indicating":[166],"limitations":[167],"in":[168,175,209],"capturing":[169],"smaller":[170],"irregular":[172],"structures":[174],"radiographs.":[177],"outcome":[179],"this":[181],"offer":[183],"important":[184],"insight":[185],"into":[186],"challenges":[188],"applying":[190],"various":[191],"deep":[192],"learning":[193],"paradigms":[194],"analysis":[198],"underscore":[200],"significance":[202],"architectural":[204],"choices":[205],"properties":[208],"achieving":[210],"clinically":[211],"relevant":[212],"outcomes.":[213]},"counts_by_year":[],"updated_date":"2026-05-01T08:36:08.643496","created_date":"2026-04-08T00:00:00"}