{"id":"W3180425562","doi":"","title":"Development and performance of a novel ‘offline’ deep learning (DL)-based glaucoma screening tool integrated on a portable smartphone-based fundus camera","year":2021,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of Toronto","funders":"","keywords":"Fundus (uterus); Glaucoma; Computer science; Fundus camera; Optometry; Ophthalmology; Artificial intelligence; Computer vision; Medicine; Retinal; Ophthalmoscopy","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":[0.0009416691,0.000264769,0.0005129441,0.0003537658,0.000412282,0.00004944896,0.0001618886,0.00008800608,0.00008335977],"category_scores_gemma":[0.001265137,0.0002249251,0.00007034443,0.001917194,0.002145581,0.0001588242,0.00008245672,0.0004870317,0.000006973021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097172,"about_ca_system_score_gemma":0.00153888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000127865,"about_ca_topic_score_gemma":0.000004284125,"domain_scores_codex":[0.9976698,0.0001141045,0.0004708728,0.0006930628,0.0005675927,0.0004846333],"domain_scores_gemma":[0.9983724,0.0002087491,0.000275268,0.0002154128,0.0006426371,0.0002855003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001534666,0.0002315748,0.52319,0.00005847838,0.00003729401,0.0002076263,0.0002893576,0.0006261498,0.4723802,0.000008808813,0.00000185433,0.00281525],"study_design_scores_gemma":[0.0009128035,0.0007568159,0.3192409,0.0004716422,0.00005057393,0.0002150817,0.000398787,0.1676339,0.5100772,0.000003395413,0.00005360663,0.0001853619],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983702,0.00008824425,0.0005020287,0.0004082077,0.00004728622,0.0001343132,0.00000234468,0.00003824617,0.0004090761],"genre_scores_gemma":[0.9453313,0.000001697096,0.05372046,0.0005204906,0.00001881555,0.00001981682,0.00003825957,0.00001618876,0.0003329701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2039491,"threshold_uncertainty_score":0.917218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05050315673129887,"score_gpt":0.3251829760493522,"score_spread":0.2746798193180534,"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."}}