{"id":"W4404107931","doi":"10.1148/ryai.240005","title":"SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"National Institute of Neurological Disorders and Stroke; Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; HORIZON EUROPE Framework Programme; Alliance de recherche numérique du Canada; Institut pour la Recherche sur la Moelle épinière et l'Encéphale; Boettcher Foundation; Ministerstvo Zdravotnictví Ceské Republiky; Institut de Valorisation des Données; Eunice Kennedy Shriver National Institute of Child Health and Human Development; European Commission; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canada First Research Excellence Fund; Canada Research Chairs; Craig H. Neilsen Foundation; Canadian Institutes of Health Research; National Science Foundation","keywords":"Medicine; Lesion; Spinal cord; Intramedullary rod; Magnetic resonance imaging; Sagittal plane; Radiology; Spinal cord injury; Segmentation; Cord; Lumbar; Surgery; Artificial intelligence; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004681218,0.001293486,0.0005816965,0.002745334,0.0004081663,0.0011567,0.0007776331,0.0009902631,0.007390984],"category_scores_gemma":[0.001063308,0.0005305986,0.0007204991,0.0007115927,0.0002397367,0.0005784781,0.001213562,0.00039763,0.003576247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004373302,"about_ca_system_score_gemma":0.001127946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005777076,"about_ca_topic_score_gemma":0.01208183,"domain_scores_codex":[0.999815,0.00001798216,0.0000145919,0.00005375013,0.00007326055,0.00002536075],"domain_scores_gemma":[0.999835,0.00003417531,0.00002190888,0.00002618583,0.00005355733,0.00002909699],"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":[0.001544481,0.0002419987,0.01242851,0.00113025,0.0005574371,0.001121133,0.0001784914,0.01704754,0.0911708,0.001240419,0.1058054,0.7675335],"study_design_scores_gemma":[0.0003809779,0.001070418,0.08510201,0.0004169872,0.0005515212,0.0112376,0.0002884603,0.570362,0.2169988,0.006674728,0.1066611,0.0002553268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3443515,0.007373989,0.439997,0.001433376,0.0006057284,0.001977637,0.0472635,0.1404028,0.01659448],"genre_scores_gemma":[0.3716342,0.002722704,0.5257468,0.0006552481,0.0003485461,0.0009618332,0.06867607,0.005441193,0.02381347],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007390984,"threshold_uncertainty_score":0.02472526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03020416481156932,"score_gpt":0.3285246762146629,"score_spread":0.2983205114030935,"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."}}