{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005818787,0.0008821503,0.001076509,0.002278086,0.0002158553,0.002600308,0.001132259,0.001574321,0.003468666],"category_scores_gemma":[0.02478622,0.0003450355,0.001174518,0.001274125,0.001183089,0.002132574,0.001033308,0.002485391,0.001039573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190791,"about_ca_system_score_gemma":0.003192276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001549363,"about_ca_topic_score_gemma":0.001459849,"domain_scores_codex":[0.9978884,0.0009139248,0.0003492401,0.000225768,0.0005570983,0.00006563136],"domain_scores_gemma":[0.9834764,0.01284763,0.0006269873,0.0004418503,0.002425344,0.0001817829],"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":[0.00008242632,0.0000862964,0.001333923,0.02344112,0.0004128026,0.0001770731,0.0002704057,0.002980209,0.0008859066,0.01853658,0.01354708,0.9382462],"study_design_scores_gemma":[0.0001560812,0.0009506266,0.008128202,0.0837862,0.001289794,0.00173552,0.0006817791,0.009834041,0.003386998,0.09672,0.7931339,0.0001969355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.00174155,0.965616,0.01212756,0.01360756,0.001719537,0.0000925923,0.0001001488,0.00009442779,0.004900698],"genre_scores_gemma":[0.02347839,0.9483143,0.02007936,0.00492395,0.002013316,0.0001592872,0.0001544398,0.00003354848,0.0008434295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005818787,"threshold_uncertainty_score":0.03077304,"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."}}