{"id":"W3170104925","doi":"10.1096/fasebj.2021.35.s1.00384","title":"Virtual Anatomist: A Deep Learning‐based Smartphone Application to Identify Complex Anatomical Features in Augmented Reality","year":2021,"lang":"en","type":"article","venue":"The FASEB Journal","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Segmentation; Human–computer interaction; Virtual reality; Deep learning; Artificial intelligence; Multimedia","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":[0.0004785174,0.0001269804,0.0002079009,0.0001273761,0.000132995,0.00004055053,0.0002651078,0.0001398591,0.0001659042],"category_scores_gemma":[0.000190266,0.000104024,0.00007455461,0.0004932153,0.00008006517,0.00004333191,0.00005357998,0.001041829,0.00004112805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001768042,"about_ca_system_score_gemma":0.00005059377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002208229,"about_ca_topic_score_gemma":0.0001987271,"domain_scores_codex":[0.9988706,0.0001440077,0.0002874592,0.0001549675,0.0002418554,0.0003010776],"domain_scores_gemma":[0.9994128,0.00009374878,0.00004552054,0.000218386,0.00005488388,0.0001745975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003669526,0.0003866103,0.00495668,0.00007989918,0.0002943557,0.0009054259,0.001245093,0.0911844,0.4402413,0.00635824,0.01489164,0.4390894],"study_design_scores_gemma":[0.00579351,0.0002886328,0.2120467,0.0002100163,0.0001190689,0.001325566,0.003171602,0.4534433,0.2354627,0.005223196,0.08197454,0.0009410903],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8652862,0.0004977093,0.1305799,0.002579314,0.0001774799,0.0001502268,0.000005545462,0.0001955915,0.0005280695],"genre_scores_gemma":[0.9989198,0.000108374,0.0003536557,0.0004119358,0.00009517201,0.00001755592,0.00002730917,0.00001734105,0.00004885568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4381483,"threshold_uncertainty_score":0.4526286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282619708289066,"score_gpt":0.2796120396626842,"score_spread":0.2667858425797935,"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."}}