{"id":"W3130028400","doi":"10.1117/1.jmi.8.1.015501","title":"Automatic segmentation and tracking of biological prosthetic heart valves","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Medicine; Tracking (education); Segmentation; Computer vision; Artificial intelligence; Biomedical engineering; Cardiology","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":[0.0003268673,0.00005956332,0.0002827901,0.00005108973,0.00002164262,0.00001472998,0.00002505218,0.00002963242,0.0002068384],"category_scores_gemma":[0.0004916643,0.00003974891,0.0003237093,0.00007254261,0.0000746632,0.00006277443,0.00002329298,0.0001216637,0.000001072091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002324412,"about_ca_system_score_gemma":0.0001883574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002433408,"about_ca_topic_score_gemma":6.747851e-8,"domain_scores_codex":[0.9989325,0.00006051347,0.0003247814,0.00007283357,0.0005182026,0.0000911901],"domain_scores_gemma":[0.999389,0.00009215881,0.000127365,0.00005512109,0.0001361327,0.0002002525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003670487,0.0003473753,0.7532625,0.0001536158,0.0002949579,0.0005494422,0.0002166361,5.098172e-7,0.007004092,0.00002921567,0.0002443583,0.2378606],"study_design_scores_gemma":[0.003109148,0.0001263568,0.9835421,0.001176315,0.0006180589,0.003745872,0.001161809,0.001715789,0.004311395,0.0003075295,0.0001235906,0.00006207669],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889821,0.005710002,0.0002896254,0.004784893,0.0001157075,0.00005729838,0.000001389841,0.000005680782,0.00005332389],"genre_scores_gemma":[0.9978507,0.0004082683,0.001189227,0.0004445152,0.00009263417,8.873132e-7,0.000003226331,0.000005249016,0.00000532428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2377986,"threshold_uncertainty_score":0.2264735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209697312046613,"score_gpt":0.3841512512949033,"score_spread":0.3620542781744371,"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."}}