{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004247436,0.001058576,0.0008449588,0.002120155,0.0007009439,0.001295911,0.0012625,0.001604584,0.002159875],"category_scores_gemma":[0.008051093,0.0004848796,0.001072304,0.0008836368,0.0005879721,0.0007953148,0.0007139529,0.0006404388,0.0009688419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007562956,"about_ca_system_score_gemma":0.001277129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008052655,"about_ca_topic_score_gemma":0.007809498,"domain_scores_codex":[0.9977927,0.0007022466,0.0002383496,0.0005068782,0.0006153154,0.0001445353],"domain_scores_gemma":[0.9929526,0.002317548,0.0004198181,0.001349179,0.002851227,0.0001094297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006103404,0.003836212,0.09263285,0.001352924,0.002466426,0.001036445,0.0009743344,0.06981859,0.2011841,0.001255247,0.005869984,0.6134695],"study_design_scores_gemma":[0.0006520674,0.00367001,0.1616812,0.0002310788,0.001660636,0.004184955,0.0005532347,0.6825133,0.1374993,0.0008425426,0.006320148,0.0001914707],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978367,0.001380665,0.09490803,0.0001006007,0.0001159227,0.0005358608,0.001466071,0.001774455,0.001881702],"genre_scores_gemma":[0.8990967,0.0005384058,0.09446699,0.0001087243,0.00003677813,0.0001903747,0.003411308,0.0003646025,0.001786179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008052655,"threshold_uncertainty_score":0.0224629,"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."}}