{"id":"W4319733470","doi":"10.1038/s41598-023-28560-w","title":"Validate your white matter tractography algorithms with a reappraised ISMRM 2015 Tractography Challenge scoring system","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université de Sherbrooke","keywords":"Tractography; Computer science; Artificial intelligence; Ground truth; Imaging phantom; Segmentation; White matter; Machine learning; Medicine; Magnetic resonance imaging; Radiology","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":[0.03623296,0.002877197,0.001824892,0.004916386,0.002151134,0.005962637,0.00285827,0.003121147,0.01979485],"category_scores_gemma":[0.1147111,0.001076587,0.002451364,0.002012768,0.001619387,0.005019716,0.006395828,0.003751272,0.02331892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001913137,"about_ca_system_score_gemma":0.003817117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005406567,"about_ca_topic_score_gemma":0.01009487,"domain_scores_codex":[0.9804867,0.005421494,0.002869846,0.002377572,0.007861348,0.0009831097],"domain_scores_gemma":[0.9045421,0.01700133,0.004291842,0.0179556,0.05111264,0.005096421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00151859,0.0007132118,0.03374941,0.001312851,0.0005613723,0.0005857357,0.0009646857,0.0164384,0.01397483,0.004974304,0.6282065,0.2970001],"study_design_scores_gemma":[0.001268227,0.002654327,0.1123968,0.001999592,0.0004783444,0.004506064,0.001232917,0.2259375,0.07601437,0.02964029,0.5426599,0.00121172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1674586,0.002436139,0.5688115,0.0120096,0.009167129,0.004943409,0.05895496,0.1290896,0.04712902],"genre_scores_gemma":[0.2093469,0.0007458827,0.5790887,0.003000822,0.001336456,0.004475749,0.1358177,0.03687535,0.02931248],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03623296,"threshold_uncertainty_score":0.1916205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07451182870939092,"score_gpt":0.349136342238855,"score_spread":0.2746245135294641,"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."}}