{"id":"W4221072717","doi":"10.1002/nbm.4730","title":"Multiple sclerosis cortical lesion detection with deep learning at ultra‐high‐field MRI","year":2022,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health; Centre d'Imagerie BioMédicale; National Institute of Mental Health; Université de Lausanne; Université de Genève; Hôpitaux Universitaires de Genève; European Commission; Multiple Sclerosis Society; Centre Hospitalier Universitaire Vaudois; Novartis Foundation; École Polytechnique Fédérale de Lausanne; Siemens Healthineers; Novartis Stiftung für Medizinisch-Biologische Forschung; National Multiple Sclerosis Society","keywords":"Multiple sclerosis; Generalizability theory; Nuclear medicine; CLs upper limits; Medicine; Reproducibility; Chemistry; Psychology","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":[0.001138444,0.00124341,0.0008380568,0.001026584,0.0003093105,0.0007715939,0.001779566,0.00154152,0.00145609],"category_scores_gemma":[0.002416652,0.0006014709,0.0009171951,0.0006322017,0.0004815894,0.001092533,0.001657636,0.001493252,0.0008044664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008661716,"about_ca_system_score_gemma":0.001008973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007110675,"about_ca_topic_score_gemma":0.01057731,"domain_scores_codex":[0.9995065,0.0001053759,0.00002569423,0.0001533324,0.0001188926,0.00009021901],"domain_scores_gemma":[0.9993879,0.0002315481,0.00008236834,0.0001105595,0.0001406335,0.00004711643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006179711,0.0004527887,0.0044324,0.0002039869,0.0003148759,0.0002637589,0.0001144272,0.5332945,0.03649184,0.001675954,0.006268119,0.4158693],"study_design_scores_gemma":[0.00001076173,0.00004968116,0.0007349427,0.000007642802,0.00001207674,0.00003290449,0.00000756767,0.9935168,0.004448615,0.0009048958,0.0002655307,0.000008573616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2961102,0.001572914,0.6908459,0.0005146195,0.00008594279,0.00021464,0.0008316562,0.007740523,0.002083608],"genre_scores_gemma":[0.8095935,0.0003756708,0.1842761,0.0002934196,0.00005310618,0.000214209,0.001946546,0.0001859349,0.00306149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007110675,"threshold_uncertainty_score":0.01413858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0488017477821324,"score_gpt":0.299257058975995,"score_spread":0.2504553111938626,"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."}}