{"id":"W4312211566","doi":"10.3390/s22249877","title":"Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Mandibular canal; Visibility; Computer vision; Deep learning; Residual; Orthodontics; Medicine; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002592559,0.0001517569,0.0001617286,0.000266499,0.0003741563,0.00005854896,0.0001150367,0.00003596067,0.000684276],"category_scores_gemma":[0.00001598958,0.0001841606,0.000205135,0.0004369048,0.00006122745,0.0001177851,0.00003418326,0.000217946,0.00000722863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002202545,"about_ca_system_score_gemma":0.00004102292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001181838,"about_ca_topic_score_gemma":0.00120838,"domain_scores_codex":[0.9984888,0.0001601771,0.0003063266,0.0003211949,0.0003521721,0.0003713161],"domain_scores_gemma":[0.9995278,0.0001091628,0.00009000017,0.0001562549,0.00003982257,0.00007700026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002826143,0.0001392392,0.408436,0.00004002176,0.00006053237,0.0002448351,0.0001089663,0.5867425,0.001449602,0.0003350358,0.001629704,0.0005309212],"study_design_scores_gemma":[0.002474586,0.00006531303,0.1934919,0.000008620264,0.00003572288,0.00004262394,0.001290319,0.8016105,0.0001182416,0.00003745535,0.0006150161,0.0002097056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942337,0.0001381555,0.003294104,0.0001997885,0.001029372,0.0004728733,0.000427071,0.0000566744,0.0001482557],"genre_scores_gemma":[0.9971232,0.000001466292,0.0003389251,0.0004244333,0.000107255,0.0001244502,0.001048887,0.00002790766,0.0008035114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2149441,"threshold_uncertainty_score":0.750985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421576557384788,"score_gpt":0.2567410511132834,"score_spread":0.2425252855394355,"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."}}