{"id":"W2899853723","doi":"10.2196/12539","title":"Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development","year":2018,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut Català de la Salut","keywords":"Diabetic retinopathy; Algorithm; Computer science; Artificial intelligence; Blindness; Machine learning; Set (abstract data type); Protocol (science); Prospective cohort study; Medicine; Optometry; Diabetes mellitus; Surgery; Pathology","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":[0.02762007,0.001960532,0.00202681,0.001628228,0.002321569,0.002103599,0.002779918,0.003102212,0.05267056],"category_scores_gemma":[0.03838184,0.001488365,0.002374685,0.001528248,0.001488487,0.001144251,0.002141083,0.004132741,0.0129996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003519809,"about_ca_system_score_gemma":0.01984051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002000951,"about_ca_topic_score_gemma":0.003601388,"domain_scores_codex":[0.986293,0.007743483,0.002437693,0.0009031681,0.001905775,0.0007169013],"domain_scores_gemma":[0.9729438,0.006721585,0.001817851,0.004469495,0.01264178,0.001405458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.05013717,0.01958493,0.0102744,0.03821392,0.001098133,0.001364289,0.003332343,0.01122675,0.01883184,0.01507647,0.1789119,0.6519479],"study_design_scores_gemma":[0.0494702,0.0269549,0.03071246,0.01948237,0.001274313,0.00152931,0.001341205,0.01302999,0.02641251,0.01563828,0.8136356,0.0005189783],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.004550504,0.0007038548,0.0272676,0.0007581318,0.0003819207,0.954904,0.004328847,0.0006515484,0.006453637],"genre_scores_gemma":[0.002729505,0.0003748206,0.05481143,0.0003256758,0.00004373724,0.93878,0.001519581,0.00004800647,0.001367308],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.05267056,"threshold_uncertainty_score":0.1762006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2224731726865407,"score_gpt":0.5357604441122716,"score_spread":0.3132872714257309,"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."}}