{"id":"W4383499136","doi":"10.1101/2023.07.05.547787","title":"Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; Wellcome / EPSRC Centre for Interventional and Surgical Sciences; Cancer Research UK; Manchester Biomedical Research Centre; Royal Marsden NHS Foundation Trust","keywords":"Repeatability; Independent component analysis; Reproducibility; Lung; SIGNAL (programming language); Nuclear medicine; Sensitivity (control systems); Magnetic resonance imaging; Medicine; Biomedical engineering; Radiology; Chemistry; Computer science; Internal medicine; Artificial intelligence; Chromatography","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":[0.001737379,0.001125149,0.0006937297,0.001593648,0.0003941164,0.0008415338,0.0006019108,0.000840219,0.001629235],"category_scores_gemma":[0.003960961,0.0004464843,0.0009458394,0.001228652,0.0003894867,0.0005541045,0.0006101382,0.0009825184,0.0008993247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003261135,"about_ca_system_score_gemma":0.0009797245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002377953,"about_ca_topic_score_gemma":0.003878498,"domain_scores_codex":[0.9994987,0.0001408422,0.00004041722,0.0001363717,0.0001312069,0.00005241822],"domain_scores_gemma":[0.9988447,0.0005110856,0.0001467799,0.0001236459,0.0003413936,0.0000323407],"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":[0.0004653294,0.0001813526,0.004264776,0.0007261903,0.0005255992,0.000460489,0.0003084124,0.03704667,0.3722006,0.002803086,0.006278908,0.5747384],"study_design_scores_gemma":[0.00008115918,0.0004924847,0.03998918,0.000115733,0.0004403181,0.0009664119,0.0001114573,0.691369,0.2400679,0.008307289,0.01782263,0.0002364201],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02959662,0.0006341316,0.9657342,0.0001453532,0.00008335865,0.0001320339,0.0002894379,0.002669127,0.0007158062],"genre_scores_gemma":[0.1887459,0.0008224192,0.8070567,0.0001475794,0.00010195,0.0005421254,0.0007204438,0.0006766876,0.001186149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002377953,"threshold_uncertainty_score":0.009188294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02077843567034077,"score_gpt":0.2691543020127266,"score_spread":0.2483758663423858,"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."}}