{"id":"W4386307409","doi":"10.18280/ts.400410","title":"Enhancing Cataract Detection Precision: A Deep Learning Approach","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Machine learning","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":[0.001232319,0.0008369464,0.0008496184,0.001583298,0.00034401,0.0009370771,0.001134868,0.001080522,0.001377537],"category_scores_gemma":[0.002845307,0.0003536811,0.0007724318,0.0008443418,0.000338805,0.0009875266,0.00105962,0.0009584287,0.0004349519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007621584,"about_ca_system_score_gemma":0.001193686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006279381,"about_ca_topic_score_gemma":0.006140737,"domain_scores_codex":[0.9993561,0.0001178976,0.00005180764,0.0001622642,0.0001777177,0.000134177],"domain_scores_gemma":[0.9990531,0.0003398048,0.0001119291,0.00008022288,0.0003620647,0.00005289076],"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":[0.0003777068,0.0004039423,0.008769681,0.000148527,0.0001583757,0.0001925989,0.00008051127,0.1752017,0.01890254,0.002538326,0.00423045,0.7889956],"study_design_scores_gemma":[0.00001181266,0.00006252115,0.001102382,0.00001705069,0.00004237374,0.00005197377,0.00001541915,0.9920633,0.004426972,0.001589059,0.0006062976,0.00001073403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1570592,0.002315774,0.8332545,0.001121646,0.0001867885,0.0001148185,0.0003205683,0.001670379,0.00395636],"genre_scores_gemma":[0.8822184,0.0007602227,0.1128307,0.0004030687,0.0001504953,0.00006259899,0.0004395088,0.00005533415,0.003079658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006279381,"threshold_uncertainty_score":0.01248568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817346419132436,"score_gpt":0.2712722337869208,"score_spread":0.2530987695955965,"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."}}