{"id":"W3173013790","doi":"10.1016/j.mri.2021.10.038","title":"Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm","year":2021,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Peripheral Nerve Disorders","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"CARE Canada","funders":"","keywords":"Epineurium; Medicine; Ghosting; Artifact (error); Radiology; Magnetic resonance imaging; Nuclear medicine; Peripheral nerve; Anatomy; Artificial intelligence; Computer science","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":[0.0001532246,0.0001830839,0.0003042188,0.00009682938,0.00009689881,0.00003863865,0.0000596791,0.00005114808,0.0006654347],"category_scores_gemma":[0.0001475098,0.0001983832,0.0001279153,0.0004460866,0.0001349006,0.0001288802,0.00003679859,0.0001740751,0.000002389279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000166486,"about_ca_system_score_gemma":0.0002012753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001945626,"about_ca_topic_score_gemma":0.0000166074,"domain_scores_codex":[0.9984706,0.00007943729,0.0004233362,0.0003848304,0.0003317102,0.0003101042],"domain_scores_gemma":[0.9991196,0.00002236223,0.00015799,0.000250259,0.0003701565,0.00007963571],"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":[0.0000504826,0.00009850822,0.04510661,0.0001006314,0.000007067446,0.0000633223,0.0001527375,0.000958539,0.1464237,0.0000127187,0.00001445156,0.8070112],"study_design_scores_gemma":[0.002705705,0.000291073,0.032903,0.0003627457,0.00007931971,0.0001894796,0.0008482212,0.9257876,0.03327978,0.00004947016,0.003260069,0.0002435402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9162089,0.01736329,0.06479173,0.000380528,0.0002814935,0.000333477,0.000004881768,0.00008832805,0.0005473706],"genre_scores_gemma":[0.9482573,0.0001398236,0.04988605,0.0005704566,0.0001351882,0.00003075954,0.00006787763,0.00006797483,0.0008445749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9248291,"threshold_uncertainty_score":0.8089834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00930878253177435,"score_gpt":0.2696731008061073,"score_spread":0.260364318274333,"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."}}