{"id":"W2514774366","doi":"10.1007/s40846-016-0152-x","title":"An Automated Approach for Localizing Retinal Blood Vessels in Confocal Scanning Laser Ophthalmoscopy Fundus Images","year":2016,"lang":"en","type":"article","venue":"Journal of Medical and Biological Engineering","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Ophthalmoscopy; Fundus (uterus); Confocal; Retinal; Fundus camera; Scanning laser ophthalmoscopy; Ophthalmology; Laser scanning; Laser; Computer science; Artificial intelligence; Medicine; Computer vision; Optics; Physics","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":[0.00132588,0.0001469204,0.0004981477,0.0001488478,0.00002967134,0.00002473573,0.0001345482,0.0001957419,0.00003652183],"category_scores_gemma":[0.0008966381,0.00007166066,0.0001124095,0.0001540388,0.0001182015,0.00008911183,0.0000286428,0.0003310888,3.746691e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002194407,"about_ca_system_score_gemma":0.00004941984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000583613,"about_ca_topic_score_gemma":5.046915e-8,"domain_scores_codex":[0.9986251,0.00004752092,0.0004953873,0.0001899562,0.000359225,0.0002828322],"domain_scores_gemma":[0.999059,0.0002415181,0.0001101402,0.00007386771,0.00009460306,0.0004208559],"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":[0.001970985,0.002023294,0.2216278,0.0009773931,0.0007860794,0.005231916,0.0002038073,0.001374002,0.701315,0.0003671194,0.000624923,0.0634976],"study_design_scores_gemma":[0.02299211,0.01068248,0.123755,0.009336548,0.0008935792,0.02155015,0.0009887227,0.7352855,0.07094642,0.0001346039,0.00218013,0.00125481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9426107,0.0006794404,0.05471194,0.001764604,0.00005784191,0.00007163643,0.000002858114,0.00005685905,0.00004413397],"genre_scores_gemma":[0.9890744,0.0002695485,0.01014842,0.0001145653,0.0003607282,0.000004101299,0.000003727873,0.00001015805,0.00001431853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7339115,"threshold_uncertainty_score":0.2922237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02424418447279895,"score_gpt":0.3163481014844395,"score_spread":0.2921039170116406,"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."}}