{"id":"W2919822225","doi":"","title":"Automated Retinal Layer Segmentation Algorithm for OCT Images: A Validation Study","year":2018,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Retinal; Computer science; Image segmentation; Artificial intelligence; Layer (electronics); Computer vision; Ophthalmology; Medicine; Materials science","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":[0.001373314,0.0001889645,0.0002992998,0.0003048493,0.0005227443,0.00008175064,0.0001971638,0.00005341027,0.00009359878],"category_scores_gemma":[0.0007714471,0.0001560518,0.00007105611,0.001324335,0.003066655,0.0003536743,0.00007538361,0.0001315153,0.00007929654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001361785,"about_ca_system_score_gemma":0.0003075024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000132215,"about_ca_topic_score_gemma":5.344139e-7,"domain_scores_codex":[0.9979228,0.0001812659,0.0003337619,0.0006657005,0.0004919276,0.0004045417],"domain_scores_gemma":[0.9983149,0.0001156958,0.0002049493,0.0002113657,0.0009179729,0.0002351333],"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":[0.00005180945,0.000367894,0.08144216,0.00001150212,0.00005851808,0.0000803221,0.00148296,0.000002716445,0.9136434,0.000005118928,0.0002681824,0.002585456],"study_design_scores_gemma":[0.001069204,0.004790808,0.145195,0.00005872016,0.0002094197,0.0003503417,0.00201957,0.05052881,0.7951166,0.0004487417,0.00001376146,0.0001990308],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969242,0.000009705738,0.0008146078,0.0004848186,0.000166082,0.0007423181,0.000008258706,0.0001667858,0.0006832519],"genre_scores_gemma":[0.9605968,4.637431e-7,0.03840063,0.0002148242,0.0001868616,0.00009356291,0.00002782269,0.00001450922,0.0004645258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1185267,"threshold_uncertainty_score":0.9996464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05879872303913238,"score_gpt":0.4202113934949664,"score_spread":0.3614126704558341,"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."}}