{"id":"W4404432079","doi":"10.1016/j.mex.2024.103052","title":"Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning","year":2024,"lang":"en","type":"article","venue":"MethodsX","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Diabetic retinopathy; Capsule; Deep learning; Artificial intelligence; Medicine; Ophthalmology; Computer science; Diabetes mellitus; Biology; Endocrinology","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":[0.001067332,0.0001687576,0.0003741358,0.0001002006,0.0002072818,0.00008894473,0.00004116308,0.00008677879,0.00004753963],"category_scores_gemma":[0.0002650913,0.0001482118,0.0001272531,0.0006341981,0.00008110609,0.00007058024,0.00004731263,0.0004589644,0.0000148546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005736967,"about_ca_system_score_gemma":0.00002323285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005231028,"about_ca_topic_score_gemma":0.000004605315,"domain_scores_codex":[0.9983618,0.0004501371,0.0002506586,0.0003914319,0.000169268,0.0003766858],"domain_scores_gemma":[0.9993767,0.000213778,0.00006490034,0.0001610483,0.00005801119,0.0001255338],"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":[0.00006590054,0.0000188819,0.007455951,0.0003229534,0.0002774026,0.0001379354,0.0009892019,0.02108313,0.5290368,0.00002848202,0.00002525186,0.4405581],"study_design_scores_gemma":[0.0002726844,0.0001483945,0.005353386,0.0005898679,0.0008451547,0.00009513387,0.000111744,0.9791083,0.01135946,0.0005002043,0.001399819,0.0002157864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.536984,0.004447092,0.4575447,0.00009076119,0.0002869345,0.00009419072,1.755215e-7,0.0001575569,0.0003946103],"genre_scores_gemma":[0.8721766,0.0002109904,0.1258844,0.00009681943,0.0009695631,0.000008691165,0.000004619554,0.00003951731,0.0006087289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9580252,"threshold_uncertainty_score":0.6043902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232127578242383,"score_gpt":0.3261311179080943,"score_spread":0.3038098421256705,"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."}}