{"id":"W3174394566","doi":"10.3390/jimaging7090165","title":"Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey","year":2021,"lang":"en","type":"preprint","venue":"Journal of Imaging","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Diabetic retinopathy; Artificial intelligence; Computer science; Optical coherence tomography; Fundus (uterus); Deep learning; Segmentation; Optometry; Medicine; Scope (computer science); Intervention (counseling); Machine learning; Ophthalmology; Diabetes mellitus","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":[0.000759559,0.0002043519,0.001013236,0.000453082,0.00004026119,0.00008440168,0.00009234973,0.0000779663,0.0000226399],"category_scores_gemma":[0.0009009164,0.0001767775,0.0003144864,0.0002621851,0.0001097301,0.00008447344,0.0001698046,0.0007668773,6.179644e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007776432,"about_ca_system_score_gemma":0.0001843182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003519215,"about_ca_topic_score_gemma":0.00000708732,"domain_scores_codex":[0.9980664,0.0003306923,0.0008170833,0.0002227918,0.0004000858,0.0001628935],"domain_scores_gemma":[0.9964737,0.0003192378,0.001172679,0.0002365605,0.001657786,0.0001399936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001007747,0.0001207065,0.9543345,0.0008607357,0.0005349059,0.000523666,0.0004197132,0.0001662244,0.02893781,9.799711e-8,0.0002310447,0.01376983],"study_design_scores_gemma":[0.0008399085,0.0001058762,0.9361848,0.004368759,0.001409065,0.0009224715,0.0005997149,0.02780513,0.0275138,0.00002336881,0.00004227727,0.0001848017],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903467,0.007739138,0.0006895316,0.0007230086,0.000321575,0.00008692915,0.00000731925,0.00003156484,0.00005418418],"genre_scores_gemma":[0.9965593,0.00162118,0.001524034,0.0001129042,0.0001233274,0.000002607757,0.0000142543,0.00002604756,0.00001638603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0276389,"threshold_uncertainty_score":0.7208776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.022566326884096,"score_gpt":0.3048020168443622,"score_spread":0.2822356899602662,"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."}}