{"id":"W2972293442","doi":"","title":"Improvement and validation of high precision ocular oximetry using a convolutional neural network algorithm and a phantom eye","year":2019,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Université Laval","funders":"","keywords":"Convolutional neural network; Imaging phantom; Computer science; Algorithm; Artificial intelligence; Medicine; Nuclear medicine","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":[0.001428421,0.0004720717,0.0003149558,0.0004852599,0.0002663514,0.0006240708,0.0006064934,0.001144609,0.0008862499],"category_scores_gemma":[0.004750384,0.0002100735,0.0003192158,0.0003419521,0.0004480295,0.0004938187,0.0006725066,0.0004590216,0.0002367129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005466783,"about_ca_system_score_gemma":0.000651352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004719377,"about_ca_topic_score_gemma":0.004732392,"domain_scores_codex":[0.9994294,0.0001236373,0.00004307059,0.0001297704,0.0002325483,0.00004142295],"domain_scores_gemma":[0.9981388,0.0007279321,0.0001138897,0.0002781662,0.0006918003,0.000049496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002355763,0.001131887,0.02412246,0.0006687008,0.000401619,0.001243706,0.0006001894,0.2000264,0.4704219,0.002751788,0.003157016,0.2931186],"study_design_scores_gemma":[0.0001048801,0.0008515074,0.01735748,0.00004626403,0.0001261908,0.0008571064,0.00007615075,0.7858394,0.192155,0.000524413,0.001999484,0.00006200727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8328114,0.0004863171,0.1626632,0.0002606642,0.0001592884,0.0001113358,0.0003515762,0.001150051,0.002006264],"genre_scores_gemma":[0.9534933,0.0001406971,0.0446803,0.00006628322,0.00001054887,0.00003117842,0.0002998613,0.000048166,0.00122968],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004719377,"threshold_uncertainty_score":0.009383798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02450558242522308,"score_gpt":0.3604715998888123,"score_spread":0.3359660174635892,"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."}}