{"id":"W4224222447","doi":"10.2967/jnmt.121.262900","title":"Validation of Convolutional Neural Networks for Fast Determination of Whole-Body Metabolic Tumor Burden in Pediatric Lymphoma","year":2022,"lang":"en","type":"article","venue":"Journal of Nuclear Medicine Technology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Universidade Estadual de Campinas","keywords":"Convolutional neural network; Medicine; Intraclass correlation; Concordance; Lymphoma; Nuclear medicine; Concordance correlation coefficient; Software; Radiology; Artificial intelligence; Computer science; Internal medicine; Statistics; Mathematics","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":[0.0007203541,0.00009813973,0.0005374743,0.001008348,0.00004936734,0.00000137727,0.000249519,0.00007921032,0.0001074523],"category_scores_gemma":[0.0004874954,0.00008334748,0.00009569414,0.0007948551,0.0002336001,0.00005106119,0.00008290302,0.0003344383,2.970719e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000783475,"about_ca_system_score_gemma":0.00009306064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001257257,"about_ca_topic_score_gemma":2.609735e-7,"domain_scores_codex":[0.9983993,0.00004652932,0.000883625,0.0001257469,0.0003857056,0.0001590548],"domain_scores_gemma":[0.9984019,0.0001180444,0.0008906428,0.0001850862,0.0003406691,0.00006364498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003485457,0.004579498,0.06465293,0.00144047,0.0003640153,0.0003962219,0.002197907,0.002180711,0.5122507,0.08213233,0.06327583,0.263044],"study_design_scores_gemma":[0.03921068,0.01932828,0.07596043,0.0008710952,0.003093614,0.009586583,0.005588018,0.6983598,0.009900047,0.02988929,0.1074369,0.0007752733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9573017,0.0007862902,0.01020083,0.03089703,0.000189292,0.0004875505,0.00001089121,0.00004509405,0.00008126066],"genre_scores_gemma":[0.9915792,0.0001090357,0.007690851,0.00023904,0.0003042191,0.00002699399,0.00001455342,0.00001979184,0.00001626861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6961791,"threshold_uncertainty_score":0.3398812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429772963634352,"score_gpt":0.2943851392163937,"score_spread":0.2800874095800502,"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."}}