{"id":"W2908650199","doi":"10.1109/tuffc.2019.2892731","title":"Live Ultrasound Color-Encoded Speckle Imaging Platform for Real-Time Complex Flow Visualization <italic>In Vivo</italic>","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Speckle pattern; Frame rate; Computer science; Computer vision; Artificial intelligence; Visualization; Optical flow; Ultrasound; Physics; Acoustics; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009043903,0.0005482124,0.0003585453,0.0006091302,0.0001887906,0.0007558921,0.00107337,0.0007855491,0.04231156],"category_scores_gemma":[0.000945302,0.0004074298,0.0002887603,0.0002509604,0.0004247637,0.0006832347,0.001041697,0.0009158066,0.01104866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003819923,"about_ca_system_score_gemma":0.0005101891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007487993,"about_ca_topic_score_gemma":0.0009173074,"domain_scores_codex":[0.9996593,0.00005629629,0.00001412,0.00007083772,0.000166169,0.00003340684],"domain_scores_gemma":[0.999435,0.0001390501,0.00005948851,0.00012828,0.000175037,0.0000632255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004632573,0.0001328015,0.0005474087,0.0002653103,0.0000214898,0.000391889,0.0001428784,0.002228193,0.7667274,0.00626454,0.03379777,0.189017],"study_design_scores_gemma":[0.0002478267,0.001137695,0.004003233,0.0001478785,0.00005718166,0.00220661,0.00007339204,0.09962776,0.6736562,0.003339519,0.2153514,0.0001513738],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02988234,0.0008555319,0.9240892,0.0007752758,0.0005112965,0.0004758329,0.001474285,0.02196614,0.01997006],"genre_scores_gemma":[0.1815493,0.001531967,0.7461634,0.001230101,0.0003301226,0.001965758,0.004121397,0.004139011,0.05896894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04231156,"threshold_uncertainty_score":0.1415462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009696696386595935,"score_gpt":0.2418196769042309,"score_spread":0.2321229805176349,"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."}}