{"id":"W3126566397","doi":"10.1109/tim.2021.3052577","title":"A Multicenter Study on Carotid Ultrasound Plaque Tissue Characterization and Classification Using Six Deep Artificial Intelligence Models: A Stroke Application","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Asymptomatic; Ultrasound; Carotid arteries; Artificial neural network; Deep learning; Stroke (engine); Pattern recognition (psychology); Characterization (materials science); Biomedical engineering; Medicine; Machine learning; Computer science; Radiology; Materials science; Pathology; Internal medicine; Engineering","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":[0.0001658761,0.0001741077,0.0001773571,0.000138912,0.0002225092,0.00008217754,0.0000263862,0.00005450674,0.00003677235],"category_scores_gemma":[0.00000776192,0.0001784536,0.00005487228,0.0001455863,0.0000405506,0.0001966503,0.000001180992,0.0001239476,0.000007736025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002015507,"about_ca_system_score_gemma":0.00007301058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002796994,"about_ca_topic_score_gemma":0.0001468223,"domain_scores_codex":[0.9984916,0.0001086472,0.0003332938,0.0004112676,0.0005177258,0.000137412],"domain_scores_gemma":[0.9993234,0.00002107688,0.00008533574,0.0002136499,0.0002213419,0.0001351723],"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":[0.0002982607,0.002414684,0.003802357,0.00008985658,0.0002195715,0.000001683328,0.002451503,0.001440123,0.8578026,0.000156471,7.495504e-7,0.1313222],"study_design_scores_gemma":[0.002931754,0.001030984,0.08762541,0.0002349765,0.001020584,0.00006798805,0.01962979,0.05234111,0.8345332,0.00006177864,0.00005933601,0.0004631061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5266389,0.00000801893,0.4722192,0.000111283,0.0001138946,0.0008276913,0.00002193773,0.00002787019,0.00003122401],"genre_scores_gemma":[0.9989301,0.0001211407,0.0003920332,0.0002057152,0.00004183468,0.0002049317,0.00006453298,0.00001925315,0.00002048994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4722912,"threshold_uncertainty_score":0.7277128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07630144649761553,"score_gpt":0.300888963169738,"score_spread":0.2245875166721225,"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."}}