{"id":"W3037365329","doi":"10.1002/mp.14351","title":"Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks","year":2020,"lang":"en","type":"article","venue":"Medical Physics","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; Hotchkiss Brain Institute; University of Calgary","funders":"Calgary Foundation; Canada Research Chairs; Heart and Stroke Foundation of Canada","keywords":"Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Computer science; Perfusion; Perfusion scanning; Pearson product-moment correlation coefficient; Ground truth; Deep learning; Nuclear medicine; Medicine; Radiology; Mathematics; Statistics","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":[0.001166653,0.001198665,0.0005136536,0.001405467,0.0002792762,0.0006973768,0.0007693446,0.0006988432,0.0006541347],"category_scores_gemma":[0.002755343,0.000302773,0.0007626569,0.0008316807,0.0003098817,0.0005694542,0.0005511133,0.0006663838,0.0003686284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008386924,"about_ca_system_score_gemma":0.0009975743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009384843,"about_ca_topic_score_gemma":0.01046105,"domain_scores_codex":[0.999615,0.0000651839,0.00002864439,0.0001430074,0.00008626838,0.00006200513],"domain_scores_gemma":[0.9993431,0.0002292725,0.0001193968,0.0000926199,0.0001848622,0.00003069921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001090868,0.0004849707,0.03735618,0.0004818685,0.0004063951,0.0007385339,0.000166535,0.2213888,0.1239273,0.0009087107,0.006600063,0.6064497],"study_design_scores_gemma":[0.00002301617,0.0001024703,0.01399769,0.00003223795,0.00005905618,0.0002464865,0.00003396607,0.9378816,0.04531849,0.000709952,0.001569986,0.00002507364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5939076,0.00185953,0.3908427,0.0003450619,0.0001063329,0.0003121992,0.002955104,0.007509828,0.002161611],"genre_scores_gemma":[0.8650447,0.0004549364,0.1272482,0.0001162865,0.00003344694,0.0002044646,0.005390057,0.0001884353,0.001319557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009384843,"threshold_uncertainty_score":0.01866043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01800441888038169,"score_gpt":0.2567437203086531,"score_spread":0.2387393014282714,"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."}}