{"id":"W4366281042","doi":"10.3390/jimaging9040084","title":"Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review","year":2023,"lang":"en","type":"review","venue":"Journal of Imaging","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Computer science; Deep learning; Convolutional neural network; Macular degeneration; Modalities; Artificial intelligence; Glaucoma; Diabetic retinopathy; Retinal; Grading (engineering); Retinal Disorder; Blindness; CAD; Machine learning; Optometry; Medicine; Ophthalmology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0009499695,0.0009832254,0.001232224,0.00281289,0.0001989038,0.0007592222,0.001020669,0.0009341082,0.003338646],"category_scores_gemma":[0.001909125,0.0003775535,0.001230897,0.002146067,0.0003051068,0.001264301,0.0007012739,0.001147696,0.001358871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005748305,"about_ca_system_score_gemma":0.001348084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002284333,"about_ca_topic_score_gemma":0.003088827,"domain_scores_codex":[0.9997203,0.00004835049,0.00005877314,0.00005219685,0.00009768894,0.00002270014],"domain_scores_gemma":[0.9992213,0.0004344978,0.00008749188,0.00001945687,0.0002054208,0.00003165209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0000505314,0.00004481255,0.0003556442,0.01647717,0.0002071797,0.00008542562,0.00002846694,0.0005780201,0.0004935268,0.0009809945,0.01641952,0.9642788],"study_design_scores_gemma":[0.00004211518,0.0003426575,0.004048417,0.02912337,0.00147598,0.00275599,0.000120467,0.001691346,0.001911181,0.004268354,0.954119,0.0001010324],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001785161,0.9984812,0.0004156429,0.0002480752,0.0001068289,0.000007783489,0.00003649898,0.00001591173,0.000509569],"genre_scores_gemma":[0.001477262,0.9973459,0.0005366582,0.0001819041,0.0001100162,0.000008971267,0.00007378413,0.000003930126,0.0002617272],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003338646,"threshold_uncertainty_score":0.01116884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07344503460586009,"score_gpt":0.4173284557208994,"score_spread":0.3438834211150393,"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."}}