{"id":"W4399259115","doi":"10.1007/s11548-024-03186-z","title":"MRI-degad: toward accurate conversion of gadolinium-enhanced T1w MRIs to non-contrast-enhanced scans using CNNs","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Contrast (vision); Gadolinium; Magnetic resonance imaging; Medicine; Radiology; Biomedical engineering; Computer science; Materials science; Artificial intelligence","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":[0.0005860589,0.001223769,0.0005513828,0.0008844024,0.0002533128,0.0009641851,0.001248833,0.000624904,0.003758387],"category_scores_gemma":[0.001435497,0.0005078125,0.0004424634,0.0006363849,0.0002347834,0.0008251938,0.00145102,0.0006849996,0.002819073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004549001,"about_ca_system_score_gemma":0.00079176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00409793,"about_ca_topic_score_gemma":0.01233644,"domain_scores_codex":[0.999739,0.00002858967,0.00001306185,0.00007595478,0.00009577824,0.00004752698],"domain_scores_gemma":[0.9997202,0.00005962198,0.00003089448,0.00006860845,0.00009571782,0.00002500771],"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":[0.0005161635,0.0001503628,0.003082024,0.0003237885,0.0001585839,0.0002928943,0.00006961993,0.01275704,0.1031525,0.003400031,0.02491263,0.8511844],"study_design_scores_gemma":[0.00005518135,0.0002036438,0.004466988,0.00009993996,0.0001165077,0.001283167,0.000102926,0.7457819,0.2005593,0.005801434,0.04145696,0.00007203879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05200457,0.0029715,0.912549,0.0005383111,0.0005022964,0.0002642253,0.00293686,0.02098649,0.007246868],"genre_scores_gemma":[0.2064589,0.001649603,0.7727637,0.0007707248,0.0001355779,0.0001735411,0.004872745,0.001038621,0.01213658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00409793,"threshold_uncertainty_score":0.01257312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.032644442935392,"score_gpt":0.3414175013693331,"score_spread":0.3087730584339411,"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."}}