{"id":"W4282978406","doi":"10.3389/frai.2022.861791","title":"Automatic Artifact Detection Algorithm in Fetal MRI","year":2022,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Hospital for Sick Children; Toronto Metropolitan University; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artifact (error); Artificial intelligence; Residual; Pattern recognition (psychology); Process (computing); Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003522616,0.0001462644,0.0002877707,0.0003853677,0.0001080151,0.00001487634,0.0001453984,0.00006650874,0.0009011598],"category_scores_gemma":[0.0001559626,0.0001440608,0.00009501886,0.001037268,0.00009308639,0.00007201528,0.00008673243,0.0006074297,0.00006191368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001658324,"about_ca_system_score_gemma":0.00005100018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001855934,"about_ca_topic_score_gemma":0.0000960585,"domain_scores_codex":[0.9983269,0.0001413489,0.0004892483,0.0003615425,0.0003259103,0.00035506],"domain_scores_gemma":[0.9995746,0.00005640674,0.00009052427,0.0001769011,0.00001671192,0.00008484797],"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":[0.0001937324,0.00041596,0.008233386,0.0000202881,0.000007929809,0.0002055777,0.0003136059,0.002078224,0.0002982655,0.00007139216,0.0003466818,0.987815],"study_design_scores_gemma":[0.0002202507,0.001142416,0.01267595,0.0000150677,0.00003132007,0.00006362976,0.002742935,0.9312573,0.003986152,0.04412701,0.003392388,0.0003456009],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7557994,0.0006564039,0.2371973,0.001352517,0.002863952,0.0008814234,0.00001797033,0.000141332,0.001089776],"genre_scores_gemma":[0.9940159,0.00006146636,0.005146354,0.0004370347,0.00008021964,0.0001096487,0.00001306253,0.00001502063,0.0001212966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9874694,"threshold_uncertainty_score":0.9867068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242951256537971,"score_gpt":0.2648373436939619,"score_spread":0.2424078311285822,"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."}}