{"id":"W4200239722","doi":"10.2147/opth.s346145","title":"FAZSeg: A New Software for Quantification of the Foveal Avascular Zone","year":2021,"lang":"en","type":"article","venue":"Clinical ophthalmology","topic":"Ophthalmology and Visual Impairment Studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Foveal avascular zone; Foveal; Medicine; Ground truth; Artificial intelligence; Ophthalmology; Software; Segmentation; Optometry; Retinal; Cartography; Computer science; Optical coherence tomography angiography; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005051812,0.0001444106,0.0006698292,0.00003001417,0.0001156223,0.000003707114,0.000155472,0.0003384718,0.0002612601],"category_scores_gemma":[0.005204191,0.0001015408,0.0005206222,0.0002387418,0.0004279812,0.00002938857,0.0001545462,0.0002800665,0.00002962278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001725252,"about_ca_system_score_gemma":0.0004479294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001317603,"about_ca_topic_score_gemma":0.00000132282,"domain_scores_codex":[0.9982092,0.0002508523,0.000735965,0.0004088555,0.0001306539,0.0002644523],"domain_scores_gemma":[0.997389,0.001266217,0.0002350046,0.0006106307,0.000378757,0.0001204027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006059023,0.001594115,0.9879815,0.0001869714,0.0004972186,0.0001706727,0.00006769957,0.000005976715,0.0004623448,0.001245466,0.004451811,0.00273032],"study_design_scores_gemma":[0.002876292,0.002550907,0.9838529,0.00007942852,0.0003970614,0.00323625,0.00008872244,0.00004241734,0.001403464,0.001958041,0.003397561,0.0001169837],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868436,0.0007394246,0.004481953,0.0056288,0.0013342,0.0005683148,0.00001077845,0.00002525432,0.0003676755],"genre_scores_gemma":[0.984091,0.00004132427,0.003698688,0.0005852848,0.0003284011,0.00003452645,0.00003288519,0.00001980669,0.0111681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01080042,"threshold_uncertainty_score":0.6230279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1669730362996346,"score_gpt":0.4645371901898653,"score_spread":0.2975641538902308,"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."}}