{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004837741,0.0005042597,0.0005250559,0.000941194,0.0003206368,0.0005435147,0.0005690516,0.0006275857,0.0007907611],"category_scores_gemma":[0.0009422142,0.0002244607,0.0005274691,0.0005175395,0.0002233861,0.0005052718,0.0004495416,0.0004313493,0.0001877493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006992808,"about_ca_system_score_gemma":0.0005056655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006706204,"about_ca_topic_score_gemma":0.004494572,"domain_scores_codex":[0.9997327,0.00003794147,0.0000164479,0.0000799471,0.00007382836,0.00005919029],"domain_scores_gemma":[0.9996675,0.0001008804,0.00004292551,0.00002974378,0.0001336305,0.00002532827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001115168,0.0005885262,0.01261861,0.000134396,0.0001599032,0.0006068723,0.0001262527,0.2064311,0.07166123,0.002648897,0.003987891,0.6999211],"study_design_scores_gemma":[0.000005562847,0.00005893565,0.002649511,0.00000624057,0.00002030613,0.0001001348,0.00000904518,0.9868907,0.009524165,0.000434092,0.0002909961,0.00001039384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4191415,0.001230991,0.5694947,0.0005839421,0.0002317326,0.0001139492,0.0003002167,0.002422934,0.006480083],"genre_scores_gemma":[0.9037185,0.0004411962,0.091934,0.0001567923,0.00004835107,0.00004246636,0.0002175741,0.0000298233,0.003411204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006706204,"threshold_uncertainty_score":0.01333433,"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."}}