{"id":"W2169539709","doi":"10.1109/cbms.2005.36","title":"Automated Optic Nerve Analysis for Diagnostic Support in Glaucoma","year":2005,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Nova Scotia Health Research Foundation","keywords":"Glaucoma; Optic nerve; Computer science; Artificial intelligence; Computer vision; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Tomography; Confocal; Feature selection; Radiology; Ophthalmology; Medicine; Optics; Physics","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":[0.0005747048,0.0003389133,0.00038569,0.001638557,0.000311395,0.0006245372,0.0004234024,0.0004655353,0.002716075],"category_scores_gemma":[0.002937903,0.0001744937,0.0002744565,0.0005020545,0.0002019405,0.0004128894,0.0003296598,0.0002392387,0.0008366294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002251927,"about_ca_system_score_gemma":0.0004745963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001126699,"about_ca_topic_score_gemma":0.002184367,"domain_scores_codex":[0.999589,0.000139392,0.00002716442,0.00004924916,0.000165918,0.00002932098],"domain_scores_gemma":[0.9984664,0.0008604136,0.0001347854,0.0001706007,0.0003257103,0.00004223573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006921406,0.0001100842,0.01443407,0.0001674576,0.00005274019,0.0005460031,0.000131453,0.01181646,0.1208181,0.001180572,0.005816181,0.8442347],"study_design_scores_gemma":[0.0001376694,0.0004203938,0.07691819,0.00008687126,0.000155678,0.003684789,0.0002596291,0.7484295,0.1485998,0.007089005,0.01412277,0.00009558856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4215848,0.002687686,0.5634573,0.0009324659,0.0001117519,0.0002256668,0.0009372553,0.00563848,0.004424731],"genre_scores_gemma":[0.7549782,0.0005441329,0.2417597,0.0001297736,0.0001298022,0.000097615,0.0007411473,0.0001195557,0.001500151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002716075,"threshold_uncertainty_score":0.009086132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322010129232295,"score_gpt":0.3099614219864457,"score_spread":0.2967413206941227,"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."}}