{"id":"W3135492365","doi":"10.1177/2292550321997012","title":"Automated Classification of Radiographic Positioning of Hand X-Rays Using a Deep Neural Network","year":2021,"lang":"en","type":"article","venue":"Plastic Surgery","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Radiography; Confusion matrix; Artificial intelligence; Data set; Artificial neural network; Deep learning; Computer science; Convolutional neural network; Test set; Set (abstract data type); Imaging phantom; Pattern recognition (psychology); Medicine; Nuclear medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002296152,0.0001312346,0.0003586603,0.0003246544,0.0001301569,0.00005693548,0.00007346999,0.00006772511,0.00008836783],"category_scores_gemma":[0.000639745,0.0001473597,0.0002996891,0.001513822,0.0001651545,0.0001932408,0.00003031992,0.0001061806,0.000003837838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001776357,"about_ca_system_score_gemma":0.00004823492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003746958,"about_ca_topic_score_gemma":0.00001413997,"domain_scores_codex":[0.9985545,0.0001474871,0.0005434453,0.0002287788,0.0002669717,0.000258816],"domain_scores_gemma":[0.9961993,0.003040344,0.0003266558,0.0002123025,0.0001571307,0.00006428357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006905208,0.0001311736,0.8768343,0.0001728583,0.0002054594,0.0001771025,0.00007830167,0.01056905,0.1099169,0.0001174958,0.0004953552,0.001232922],"study_design_scores_gemma":[0.0001882716,0.000007644189,0.6976883,0.0002645236,0.0001282207,0.0002441644,0.0001144497,0.2962513,0.004926183,0.00004653323,0.00001259014,0.0001277676],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987834,0.001373565,0.007496509,0.000004935408,0.003033739,0.00004010122,0.00001836624,0.00009284184,0.0001059422],"genre_scores_gemma":[0.9990468,0.00001635174,0.0007154732,0.000008604302,0.0001085698,0.000003447146,0.00007809717,0.00001987112,0.000002757304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2856823,"threshold_uncertainty_score":0.6009154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02670652683647055,"score_gpt":0.2598517644220728,"score_spread":0.2331452375856022,"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."}}