{"id":"W4413355910","doi":"10.1016/j.compbiomed.2025.110960","title":"Automatic margin line extraction using 3D deep learning on digital surface models of prepared teeth for crown generation","year":2025,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"InterDigital (Canada); Polytechnique Montréal","funders":"MEDTEQ+; Natural Sciences and Engineering Research Council of Canada; King Fahd University of Petroleum and Minerals","keywords":"Crown (dentistry); Margin (machine learning); Computer science; Line (geometry); Extraction (chemistry); Artificial intelligence; Surface (topology); Deep learning; Geology; Pattern recognition (psychology); Orthodontics; Machine learning; Mathematics; Chemistry; Geometry; Medicine; Chromatography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004043124,0.001110138,0.0009941435,0.001257609,0.0002800478,0.000977674,0.001149725,0.001390042,0.00422725],"category_scores_gemma":[0.0008711145,0.0008103846,0.001293851,0.0007557091,0.0002857513,0.0007420198,0.001169519,0.001279132,0.002561391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004044501,"about_ca_system_score_gemma":0.001166209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003200727,"about_ca_topic_score_gemma":0.007319964,"domain_scores_codex":[0.9997007,0.00001813272,0.00001730667,0.0000848192,0.0001202956,0.00005882824],"domain_scores_gemma":[0.9995413,0.000111613,0.00005350097,0.00009037958,0.0001722228,0.00003105146],"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":[0.0005266952,0.0002239829,0.00295807,0.0002752239,0.0000695737,0.0002551917,0.0001294774,0.07340258,0.1058175,0.001620445,0.009342803,0.8053785],"study_design_scores_gemma":[0.00001608642,0.00006250679,0.001380318,0.00002760823,0.00003079098,0.0001157624,0.00003577373,0.9657711,0.02872077,0.001157033,0.00265881,0.00002338628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07905492,0.0005439885,0.9059381,0.0002216123,0.0001321919,0.0001804056,0.001087191,0.01100804,0.00183346],"genre_scores_gemma":[0.4226393,0.000496802,0.5692044,0.0001516505,0.00004322303,0.0001690637,0.002613992,0.0008485064,0.003832887],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00422725,"threshold_uncertainty_score":0.01414156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03005530910969555,"score_gpt":0.3473871386434293,"score_spread":0.3173318295337338,"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."}}