{"id":"W4281257164","doi":"10.1016/j.acra.2022.04.023","title":"Automatic Segmentation and Quantification of Upper Airway Anatomic Risk Factors for Obstructive Sleep Apnea on Unprocessed Magnetic Resonance Images","year":2022,"lang":"en","type":"article","venue":"Academic Radiology","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institutes of Health","keywords":"Sørensen–Dice coefficient; Magnetic resonance imaging; Tongue; Segmentation; Airway; Obstructive sleep apnea; Computer science; Correlation coefficient; Artificial intelligence; Pattern recognition (psychology); Hyoid bone; Medicine; Convolutional neural network; Radiology; Image segmentation; Anatomy; Pathology; Internal medicine; Machine learning; Surgery","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":[0.0008611078,0.0005180558,0.0004803089,0.002032355,0.0003962515,0.001269851,0.0004673427,0.0008275684,0.001399704],"category_scores_gemma":[0.001870481,0.0003840269,0.0005860896,0.000641953,0.000292106,0.0004268936,0.0004212334,0.0003700254,0.0005030816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002376224,"about_ca_system_score_gemma":0.0008712947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004499021,"about_ca_topic_score_gemma":0.009603214,"domain_scores_codex":[0.9997346,0.00004678909,0.00002718295,0.0000703301,0.00007644948,0.00004477656],"domain_scores_gemma":[0.9995089,0.0001866833,0.00005976928,0.0000444326,0.0001700827,0.00003022992],"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":[0.001151286,0.0002865664,0.05474575,0.0004844008,0.0003447334,0.0008880627,0.0006333716,0.007760298,0.3376312,0.001245789,0.003481586,0.591347],"study_design_scores_gemma":[0.0001234283,0.0005119676,0.4534563,0.0001974377,0.000670666,0.004742333,0.0006282549,0.3882967,0.1396715,0.003578936,0.007981503,0.0001409463],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6642693,0.002391809,0.3258215,0.0003719972,0.0001386145,0.0003765581,0.001261204,0.003134041,0.002235149],"genre_scores_gemma":[0.7300281,0.0008196012,0.2650007,0.0001194581,0.0001122321,0.0001341337,0.001362116,0.0003068495,0.002116704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004499021,"threshold_uncertainty_score":0.008945644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0184706018363666,"score_gpt":0.3089064477036851,"score_spread":0.2904358458673185,"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."}}