{"id":"W2993395888","doi":"","title":"Deep Neural Network Based Quantification of Retinal Optical Coherence Tomography Images","year":2018,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Optical coherence tomography; Retinal; Coherence (philosophical gambling strategy); Computer science; Tomography; Artificial neural network; Artificial intelligence; Ophthalmology; Optometry; Optics; Medicine; Physics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001074463,0.0001833714,0.000368847,0.0002530497,0.000303148,0.00003688032,0.0003357658,0.00007384425,0.0001523252],"category_scores_gemma":[0.001023034,0.0001499945,0.0001161522,0.002424549,0.01580496,0.0001867616,0.00008088814,0.0002345329,0.00003314305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003413997,"about_ca_system_score_gemma":0.0002498736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007086872,"about_ca_topic_score_gemma":0.000001098945,"domain_scores_codex":[0.9978111,0.0001705942,0.000388364,0.0006098047,0.0005336903,0.0004863816],"domain_scores_gemma":[0.9980422,0.0002068568,0.0002461647,0.0003323668,0.0008504177,0.0003219715],"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":[0.00008123197,0.00009919784,0.3470504,0.00001753116,0.00001585255,0.00005390746,0.00008743591,0.00009634954,0.6518534,0.0001310678,0.00004832508,0.0004652693],"study_design_scores_gemma":[0.0002134496,0.001313696,0.4685482,0.00009087597,0.00008065385,0.0001849619,0.00008834004,0.04024459,0.4881858,0.0009134697,0.000004784889,0.0001311571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949659,0.0000775556,0.0006313537,0.001124942,0.000137745,0.000165225,0.000001725106,0.00003748695,0.002858104],"genre_scores_gemma":[0.9822257,7.725079e-7,0.01723807,0.0002894556,0.0001561203,0.00001274194,0.000008027024,0.000009838731,0.00005935157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1636676,"threshold_uncertainty_score":0.9868734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04389687086643072,"score_gpt":0.3618955467444407,"score_spread":0.31799867587801,"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."}}