{"id":"W3211126657","doi":"10.32920/ryerson.14648760.v1","title":"Automatic detection and classification of diabetic retinopathy from retinal fundus images","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Diabetic retinopathy; Adaptive histogram equalization; Artificial intelligence; Thresholding; Fundus (uterus); Computer science; Retinal; Support vector machine; Retinopathy; Ophthalmology; Blindness; Computer vision; Retina; Medicine; Pattern recognition (psychology); Histogram; Optometry; Diabetes mellitus; Histogram equalization; Image (mathematics); Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002639299,0.0002030196,0.0006106299,0.0001733455,0.00003852933,0.00007049955,0.00006898081,0.0001982553,0.0003805847],"category_scores_gemma":[0.0003171032,0.000176745,0.0001768177,0.0001604853,0.0001240745,0.00003866569,0.0001461567,0.0004688283,0.000008357647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005399906,"about_ca_system_score_gemma":0.00008523461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005568723,"about_ca_topic_score_gemma":0.00001485864,"domain_scores_codex":[0.99845,0.0001343885,0.0004816427,0.0004965014,0.0003009476,0.0001365596],"domain_scores_gemma":[0.9986777,0.0001086743,0.0003259149,0.0005364813,0.0002530408,0.00009824261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006493895,0.000219322,0.103591,0.002046383,0.0004773554,0.0000579246,0.0005713031,0.000005856814,0.7442925,0.000008356375,0.0002104802,0.1484546],"study_design_scores_gemma":[0.0004590056,0.0001147647,0.8210852,0.001780534,0.001940806,0.0000369845,0.001077406,0.0717645,0.1010511,0.0004009352,0.00002789724,0.0002608506],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910009,0.001133953,0.004131494,0.0009674105,0.0001327298,0.0001772354,0.00001179899,0.0001003424,0.002344128],"genre_scores_gemma":[0.9924201,0.000266314,0.006319279,0.00004736561,0.0001124162,0.00002111567,0.0001654412,0.00002207013,0.0006258441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7174942,"threshold_uncertainty_score":0.7207452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02150200368979546,"score_gpt":0.2787597510299916,"score_spread":0.2572577473401961,"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."}}