{"id":"W4308529557","doi":"10.1016/j.jdent.2022.104345","title":"Temporomandibular joint segmentation in MRI images using deep learning","year":2022,"lang":"en","type":"article","venue":"Journal of Dentistry","topic":"Temporomandibular Joint Disorders","field":"Health Professions","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Telus (Canada); University of Alberta","funders":"","keywords":"Temporomandibular joint; Condyle; Segmentation; Magnetic resonance imaging; Artificial intelligence; Computer science; Convolutional neural network; Population; Medicine; Anatomy; Orthodontics; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002528715,0.000772635,0.0005545166,0.001406082,0.0003151826,0.0008491093,0.000663765,0.001084468,0.00220233],"category_scores_gemma":[0.0006198023,0.0004907757,0.001050607,0.000970086,0.0002397147,0.0004065055,0.0005766098,0.0008189041,0.001339597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005281682,"about_ca_system_score_gemma":0.001222734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0168223,"about_ca_topic_score_gemma":0.0253119,"domain_scores_codex":[0.9998618,0.00001197243,0.000009361366,0.00004180647,0.00003396648,0.00004094886],"domain_scores_gemma":[0.9998641,0.00003466567,0.00002354076,0.0000180861,0.0000461625,0.00001336799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000539328,0.0002551475,0.008135113,0.0003690004,0.0002662282,0.0008070342,0.0001748229,0.08838403,0.07652541,0.001738198,0.01347822,0.8093274],"study_design_scores_gemma":[0.00002239481,0.00009452525,0.006968422,0.00006614923,0.000115989,0.0006054689,0.00009392955,0.9503479,0.03218798,0.003730894,0.005729732,0.00003663193],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1782969,0.003399002,0.8007208,0.001046661,0.000304176,0.0002420747,0.002773636,0.007546404,0.005670411],"genre_scores_gemma":[0.6605945,0.002109598,0.3204669,0.0006056352,0.0002100324,0.0001827984,0.004421304,0.0005829416,0.0108264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0168223,"threshold_uncertainty_score":0.03344876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04395717711066265,"score_gpt":0.3874302798147715,"score_spread":0.3434731027041088,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}