{"id":"W4408090715","doi":"10.3390/app15052684","title":"From Pixels to Diagnosis: Early Detection of Diabetic Retinopathy Using Optical Images and Deep Neural Networks","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Diabetic retinopathy; Artificial intelligence; Medicine; Pixel; Computer science; Optometry; Ophthalmology; Computer vision; Pattern recognition (psychology); 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":[0.0002510269,0.00008119256,0.0002135887,0.0001387134,0.0001266842,0.00005995822,0.0000827175,0.00003256298,0.000005958635],"category_scores_gemma":[0.00008524938,0.00006378478,0.00003629158,0.0006496565,0.0003242095,0.00004792384,0.00005482695,0.0000887034,0.000001189052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000116892,"about_ca_system_score_gemma":0.00001335507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002397591,"about_ca_topic_score_gemma":0.000006310313,"domain_scores_codex":[0.9991825,0.00001959456,0.0001720926,0.0002781178,0.0001766614,0.0001710721],"domain_scores_gemma":[0.9995713,0.000161998,0.00004337663,0.000110594,0.00003575992,0.0000769478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000829475,0.00007675729,0.2712113,0.00003899631,0.000055425,0.000008032829,0.0005365597,0.002385349,0.5725455,0.0001268313,0.00001904215,0.1529132],"study_design_scores_gemma":[0.0006999287,0.0004009254,0.3738605,0.0003260809,0.0007847878,0.000008356746,0.001961272,0.2846061,0.335698,0.001326294,0.00002516829,0.0003025273],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790055,0.0004765177,0.01919735,0.0005457684,0.00006884935,0.0001094579,6.447108e-7,0.0000198675,0.0005760939],"genre_scores_gemma":[0.9958268,0.00001404739,0.003825034,0.0002398309,0.00006572768,0.00001416431,5.108591e-7,0.000003378416,0.00001049158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2822208,"threshold_uncertainty_score":0.2601068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01201985183667676,"score_gpt":0.2805422676696561,"score_spread":0.2685224158329794,"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."}}