{"id":"W4390145386","doi":"10.18280/isi.280624","title":"Enhanced Detection of Diabetic Retinopathy Using Ensemble Machine Learning: A Comparative Study","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diabetic retinopathy; Artificial intelligence; Ensemble learning; Computer science; Retinopathy; Machine learning; Medicine; Optometry; Diabetes mellitus; Endocrinology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004798239,0.0001334949,0.0003766769,0.0004820335,0.0001772686,0.0000442366,0.00005213318,0.00004546288,0.00001475256],"category_scores_gemma":[0.0002982796,0.0001196851,0.00009029155,0.001104487,0.0000752477,0.0004963219,0.00003410731,0.0001676955,0.0000699783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001222049,"about_ca_system_score_gemma":0.00003932076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002286141,"about_ca_topic_score_gemma":0.00001031755,"domain_scores_codex":[0.9987515,0.0001118623,0.0005383408,0.000109146,0.0002970624,0.0001920455],"domain_scores_gemma":[0.9990483,0.00005936216,0.0003466296,0.0001581407,0.0003295253,0.00005807863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000676368,0.0003111063,0.1113789,0.001387269,0.0005363778,0.00001905185,0.09292053,0.01152399,0.7124361,0.00002682514,0.00002911175,0.06875434],"study_design_scores_gemma":[0.002096821,0.001675356,0.07741409,0.0005968255,0.0005259883,0.00005363611,0.03081772,0.5238495,0.3623345,0.0001750436,0.0001243288,0.0003360918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839517,0.00003930225,0.01376677,0.00001113602,0.00007489561,0.0003491945,0.000002802297,0.0001722315,0.001631972],"genre_scores_gemma":[0.9995055,0.00001016198,0.0002254931,0.00001313161,0.00002707281,0.00002152707,0.00005944336,0.000009391364,0.0001283145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5123256,"threshold_uncertainty_score":0.4880616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03074828514223635,"score_gpt":0.2943467709007509,"score_spread":0.2635984857585145,"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."}}