{"id":"W3096061254","doi":"10.1167/tvst.9.2.55","title":"Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice","year":2020,"lang":"en","type":"article","venue":"Translational Vision Science & Technology","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Moorfields Eye Hospital NHS Foundation Trust; Heidelberg Engineering; Moorfields Eye Charity; Academy of Medical Sciences; Carl Zeiss Meditec AG; National Institute for Health and Care Research; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; Allergan; UK Research and Innovation","keywords":"Glaucoma; Fundus photography; Artificial intelligence; Machine learning; Computer science; Optical coherence tomography; Algorithm; Receiver operating characteristic; Medicine; Gonioscopy; Fundus (uterus); Ophthalmology; Visual acuity","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":[],"consensus_categories":[],"category_scores_codex":[0.001312895,0.0001823662,0.0003581247,0.0007350965,0.0002819397,0.0000874747,0.0003180293,0.0001501118,0.00004925565],"category_scores_gemma":[0.002120957,0.0001534491,0.00009249413,0.004473931,0.0007065302,0.000371803,0.00007079008,0.0003928899,0.0001126111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002630119,"about_ca_system_score_gemma":0.0001747818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007037319,"about_ca_topic_score_gemma":0.000005519339,"domain_scores_codex":[0.9971109,0.00006692119,0.0007089686,0.0008642887,0.0008998989,0.0003490415],"domain_scores_gemma":[0.9982727,0.0004018889,0.0001039186,0.0002758553,0.0004202654,0.0005253942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003629266,0.000125079,0.01777149,0.0000115545,0.00001136341,0.00004987823,0.0003804362,0.00001856704,0.02459255,0.0009801296,0.00004539845,0.9556506],"study_design_scores_gemma":[0.00214497,0.01792146,0.3333436,0.001072398,0.000996522,0.001592691,0.00282086,0.3332167,0.1670685,0.02347145,0.114102,0.002248943],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3882842,0.0003711609,0.3336706,0.2765388,0.00009591733,0.0005600553,0.000005314034,0.0002197552,0.0002542124],"genre_scores_gemma":[0.7742382,0.00003762064,0.223149,0.002405018,0.0001281487,0.00002275108,0.000003834116,0.00001099067,0.000004462166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9534017,"threshold_uncertainty_score":0.6257474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06925677442073568,"score_gpt":0.4542396510534507,"score_spread":0.384982876632715,"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."}}