{"id":"W4399728115","doi":"10.1109/access.2024.3415617","title":"Deep Learning in Automatic Diabetic Retinopathy Detection and Grading Systems: A Comprehensive Survey and Comparison of Methods","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"University of Sharjah; University of Windsor","keywords":"Computer science; Diabetic retinopathy; Grading (engineering); Artificial intelligence; Medicine; Diabetes mellitus; Engineering","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":[0.003663101,0.001588019,0.001274491,0.003263788,0.0003651978,0.001618479,0.001460079,0.001141487,0.001632103],"category_scores_gemma":[0.005626878,0.0005974005,0.001131084,0.002016781,0.0003692996,0.001608846,0.001081624,0.001359182,0.001137265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009598759,"about_ca_system_score_gemma":0.001072716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006533629,"about_ca_topic_score_gemma":0.005359535,"domain_scores_codex":[0.9983587,0.0003683512,0.0002166286,0.0003543136,0.0005971224,0.0001050026],"domain_scores_gemma":[0.9979929,0.0009527941,0.0001324944,0.0001744098,0.0006887351,0.00005869359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001850016,0.0001513304,0.004630282,0.000893,0.0001973707,0.00003998526,0.00005748879,0.01829122,0.001560558,0.001449627,0.006717302,0.9658269],"study_design_scores_gemma":[0.0001266443,0.001210317,0.02001067,0.003261907,0.0009436462,0.0009644174,0.0004084508,0.8200575,0.03075293,0.01653047,0.1054875,0.0002456143],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0593957,0.2985485,0.6157483,0.002947323,0.0008909499,0.0004361483,0.002043597,0.005263517,0.01472592],"genre_scores_gemma":[0.481693,0.1968178,0.3007881,0.002043625,0.0007832361,0.0004162636,0.006840526,0.000663039,0.009954351],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006533629,"threshold_uncertainty_score":0.01937258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06866007820158837,"score_gpt":0.4224628938747202,"score_spread":0.3538028156731319,"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."}}