{"id":"W4360989151","doi":"10.18280/ria.370113","title":"Early Diabetic Retinopathy Detection Using Convolution Neural Network","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diabetic retinopathy; Convolution (computer science); Retinopathy; Computer science; Convolutional neural network; Artificial neural network; Medicine; Artificial intelligence; Ophthalmology; Pattern recognition (psychology); 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":[0.0004263024,0.0001388054,0.0002502788,0.0001727658,0.000223536,0.0000462505,0.00008306756,0.00007026808,0.00007904715],"category_scores_gemma":[0.0001586197,0.0001348904,0.0001691985,0.001428119,0.00009384181,0.00008530959,0.00004053718,0.000226776,0.0007552156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006417794,"about_ca_system_score_gemma":0.00001801392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001062001,"about_ca_topic_score_gemma":0.000003304606,"domain_scores_codex":[0.9986345,0.00006901875,0.0003613489,0.0003328621,0.0001841315,0.0004181277],"domain_scores_gemma":[0.9992622,0.00008201338,0.0001001838,0.0003276517,0.0001093398,0.0001186796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002152584,0.0001601879,0.1163331,0.0003074953,0.0001293544,0.0003032901,0.001526508,0.4836912,0.2825533,0.0002188045,0.0005692683,0.1139922],"study_design_scores_gemma":[0.00004337685,0.0001391505,0.007754239,0.0001684925,0.0001238263,0.00006683511,0.0003179942,0.9565159,0.03402189,0.0002966352,0.0004095704,0.0001420726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765437,0.0002139447,0.02143169,0.0004625226,0.0004193366,0.0001698611,0.000001107509,0.0002425859,0.0005152288],"genre_scores_gemma":[0.996976,0.00005798811,0.0002875816,0.00008887913,0.0004075489,0.000009453998,0.00001200859,0.00002549244,0.002135036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4728247,"threshold_uncertainty_score":0.9707021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04829747259511299,"score_gpt":0.2964337135757182,"score_spread":0.2481362409806052,"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."}}