{"id":"W4386830609","doi":"10.18280/ria.370425","title":"Diabetic Retinopathy Severity Categorization in Retinal Images 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":"Categorization; Retinal; Convolutional neural network; Convolution (computer science); Computer science; Diabetic retinopathy; Artificial intelligence; Retinopathy; Pattern recognition (psychology); Artificial neural network; Ophthalmology; Optometry; Medicine; 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":[0.0006863411,0.0001567863,0.0003090552,0.000226615,0.0001435772,0.00004340155,0.0001012552,0.0000753675,0.00009231075],"category_scores_gemma":[0.0002707125,0.0001578529,0.0001156455,0.001897697,0.0001153677,0.0001150658,0.00005399776,0.0002700753,0.0002331227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001020919,"about_ca_system_score_gemma":0.00003864647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001763504,"about_ca_topic_score_gemma":0.000007442408,"domain_scores_codex":[0.9983608,0.0001177771,0.0004784112,0.0003886318,0.0001986655,0.0004557256],"domain_scores_gemma":[0.9992635,0.00009301543,0.0001179088,0.0003104169,0.0001165905,0.00009857617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001627533,0.0001859608,0.4913107,0.0003500092,0.00003499807,0.0004031847,0.001193015,0.4285759,0.05770027,0.0003556667,0.001209616,0.01851797],"study_design_scores_gemma":[0.00006414087,0.00007048721,0.02148336,0.0002607269,0.00006784769,0.00006398434,0.0004922068,0.9626477,0.01382348,0.0006883575,0.0001702286,0.0001674823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782134,0.0002668776,0.01894185,0.001233949,0.0002780271,0.000224527,0.000002698451,0.0001562841,0.0006824004],"genre_scores_gemma":[0.997384,0.0001414978,0.0007059494,0.00009560575,0.0002468591,0.00001010745,0.00006334707,0.00002319505,0.001329414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5340719,"threshold_uncertainty_score":0.6437055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0422947354637838,"score_gpt":0.3036563156568667,"score_spread":0.2613615801930829,"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."}}