{"id":"W4396223241","doi":"10.1007/s00330-024-10769-6","title":"Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm","year":2024,"lang":"en","type":"article","venue":"European Radiology","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Neuroradiology; Interventional radiology; Medicine; Contrast (vision); Artificial intelligence; Radiology; Ultrasound; Algorithm; Phase contrast microscopy; Pattern recognition (psychology); Computer science; Neurology; Physics; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007520054,0.001109011,0.0007428335,0.001940614,0.0004952597,0.001671242,0.001410397,0.001422479,0.003400125],"category_scores_gemma":[0.002894996,0.0003469765,0.0009719108,0.0008919065,0.0002777633,0.0007725334,0.0008813214,0.000893725,0.001748176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007160044,"about_ca_system_score_gemma":0.001295163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00421202,"about_ca_topic_score_gemma":0.004391986,"domain_scores_codex":[0.9994066,0.00007249725,0.00006239161,0.0001980182,0.0002064156,0.00005398333],"domain_scores_gemma":[0.999136,0.0002775806,0.00007088765,0.0000842263,0.0003900391,0.00004130316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00065451,0.000737651,0.006867688,0.0001694959,0.0002086084,0.0005306992,0.00008746178,0.07262711,0.03790484,0.002332424,0.01367031,0.8642092],"study_design_scores_gemma":[0.00005469011,0.00008515619,0.00223482,0.00002006271,0.00006854876,0.0002220863,0.00001959196,0.9724273,0.01974423,0.001684952,0.003414517,0.00002402452],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06148819,0.0003719154,0.909193,0.0003452417,0.0001760841,0.0003063479,0.0008792828,0.02330031,0.003939521],"genre_scores_gemma":[0.2391698,0.0001560648,0.7532639,0.0002630476,0.00009510783,0.0002166463,0.00234786,0.0004584971,0.004029023],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00421202,"threshold_uncertainty_score":0.01137453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05173493165075577,"score_gpt":0.3353399726209769,"score_spread":0.2836050409702212,"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."}}