{"id":"W2980254319","doi":"10.1109/embc.2019.8857060","title":"Fully Automated Segmentation of Alveolar Bone Using Deep Convolutional Neural Networks from Intraoral Ultrasound Images","year":2019,"lang":"en","type":"article","venue":"","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Segmentation; Hausdorff distance; Ultrasound; Dental alveolus; Ground truth; Deep learning; Radiography; Computer vision; Pattern recognition (psychology); Radiology; Medicine; Orthodontics","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":[0.0005587957,0.0007222205,0.0004653543,0.001362342,0.0002367585,0.0006746115,0.0008221579,0.001012762,0.0008695779],"category_scores_gemma":[0.0008966253,0.0004509141,0.0006650723,0.0006083959,0.0003747217,0.000593578,0.0005438532,0.0006123827,0.0003906947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005707807,"about_ca_system_score_gemma":0.0008510704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007597448,"about_ca_topic_score_gemma":0.01772384,"domain_scores_codex":[0.9996836,0.00004302571,0.00001945925,0.00008654485,0.0001107778,0.00005659173],"domain_scores_gemma":[0.9996842,0.00009996496,0.00005979151,0.00004488054,0.00009533967,0.00001582425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003663858,0.0001672827,0.003159157,0.0001572552,0.0001211158,0.0003134356,0.000153764,0.2078953,0.1376397,0.001756785,0.001959596,0.6463103],"study_design_scores_gemma":[0.000004622242,0.00003889144,0.001677807,0.00001129043,0.00001810114,0.0001037436,0.00002240968,0.9778073,0.01875794,0.0008413419,0.0007075006,0.000008961946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1113005,0.0006682146,0.8847975,0.0001231419,0.00003753098,0.00007024172,0.0001902053,0.00157471,0.001237909],"genre_scores_gemma":[0.6020536,0.0005774053,0.3927871,0.0001272131,0.00003187003,0.00007997476,0.0007902912,0.0001657481,0.003386748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007597448,"threshold_uncertainty_score":0.01510644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00829606782996054,"score_gpt":0.2554174710629855,"score_spread":0.2471214032330249,"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."}}