{"id":"W2985048797","doi":"10.1002/jbio.201960083","title":"Full‐field swept‐source optical coherence tomography and neural tissue classification for deep brain imaging","year":2019,"lang":"en","type":"article","venue":"Journal of Biophotonics","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University of Toronto","funders":"University of Toronto","keywords":"Optical coherence tomography; Neuroimaging; Tomography; Coherence (philosophical gambling strategy); Optical tomography; Biomedical engineering; Deep brain stimulation; Brain tumor; Preclinical imaging; Optics; Computer science; Neuroscience; Artificial intelligence; Medicine; Physics; In vivo; Pathology; Biology","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":[0.0004189218,0.0004606335,0.0002597227,0.0009904451,0.0001737645,0.0004604966,0.0002854611,0.0006892521,0.001074858],"category_scores_gemma":[0.000847962,0.000289181,0.0002301514,0.0008215726,0.0006043913,0.00106377,0.0004457719,0.0003956919,0.0002410344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003987166,"about_ca_system_score_gemma":0.0004413316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006530486,"about_ca_topic_score_gemma":0.002062828,"domain_scores_codex":[0.999663,0.00007595488,0.0000188557,0.00006488864,0.0001488445,0.00002852497],"domain_scores_gemma":[0.9995096,0.0002126022,0.0001131722,0.00004421768,0.00009181465,0.00002857689],"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":[0.0001288676,0.00004578382,0.001812503,0.0002386342,0.00004697512,0.0001060119,0.00005626342,0.00348275,0.8811189,0.002350978,0.0006404956,0.1099719],"study_design_scores_gemma":[0.00006163255,0.0005062252,0.01934245,0.00007716194,0.00009117374,0.001718861,0.0001313794,0.2534385,0.7076061,0.006258999,0.01064281,0.0001246253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2383697,0.005756399,0.751112,0.0007319606,0.00006751144,0.0001493817,0.0004063734,0.0006137402,0.002793065],"genre_scores_gemma":[0.5917041,0.002413111,0.4030312,0.0002773122,0.00007585502,0.0001273792,0.0003670984,0.00005497795,0.001949084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001074858,"threshold_uncertainty_score":0.00359571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008130559284290533,"score_gpt":0.2411743897943521,"score_spread":0.2330438305100616,"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."}}