{"id":"W4411349534","doi":"10.1007/s00429-025-02938-0","title":"Think deep in the tractography game: deep learning for tractography computing and analysis","year":2025,"lang":"en","type":"review","venue":"Brain Structure and Function","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Tractography; Deep learning; Computer science; Artificial intelligence; Data science; Psychology; Medicine; Diffusion MRI; Radiology; Magnetic resonance imaging","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":[0.0002161302,0.000269651,0.0008191808,0.0007168693,0.0001818489,0.00005547345,0.00007688181,0.0002180816,0.000004186357],"category_scores_gemma":[0.00009296731,0.0001786675,0.0003983994,0.001621139,0.00006722534,0.00004588331,0.00002274112,0.0006829735,5.105257e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001067962,"about_ca_system_score_gemma":0.0000181702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005232427,"about_ca_topic_score_gemma":0.00001168107,"domain_scores_codex":[0.9988194,0.00009950718,0.000342027,0.0004565395,0.0001075945,0.0001749752],"domain_scores_gemma":[0.998638,0.0008234186,0.0002193253,0.0002408439,0.00003758137,0.00004087885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001502897,0.00001373428,0.0005011354,0.002551992,0.0002060376,0.000001435183,0.0001810162,0.0000124708,0.000002340973,0.000461588,0.00005496304,0.9959983],"study_design_scores_gemma":[0.000427944,0.0001744344,0.01537641,0.001350266,0.007357187,0.00008392344,0.0001985459,0.001216851,4.484e-7,0.003326848,0.9702034,0.0002837703],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002049684,0.8240647,0.1739519,0.0002360319,0.00004091877,0.001304464,0.0000176469,0.0000901279,0.00008930545],"genre_scores_gemma":[0.009389685,0.9844784,0.004546357,0.0007146422,0.0001481543,0.00007907467,0.0005899786,0.00002754817,0.00002621389],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9957145,"threshold_uncertainty_score":0.7285847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375072719671957,"score_gpt":0.3613528485273932,"score_spread":0.3276021213306737,"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."}}