{"id":"W4388666866","doi":"10.1109/bhi58575.2023.10313520","title":"Multimodal Deep Learning for Pediatric Mild Traumatic Brain Injury Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Traumatic Brain Injury Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of British Columbia; Stollery Children's Hospital; Centre Hospitalier Universitaire Sainte-Justine; University of Alberta; Université de Montréal; Children's Hospital of Eastern Ontario; University of Calgary","funders":"","keywords":"Artificial intelligence; Modalities; Deep learning; Computer science; Traumatic brain injury; Diffusion MRI; Classifier (UML); Magnetic resonance imaging; Pattern recognition (psychology); Machine learning; Medicine; Radiology","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":[0.0005933396,0.0008940613,0.000493252,0.0006000755,0.0001933947,0.0003582764,0.0006587401,0.0006242445,0.00160814],"category_scores_gemma":[0.001305587,0.0001956476,0.0005091223,0.0004137383,0.000201147,0.0005004244,0.0007352028,0.000912615,0.0004679657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005726058,"about_ca_system_score_gemma":0.0008472125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005869761,"about_ca_topic_score_gemma":0.008556037,"domain_scores_codex":[0.999733,0.00006877573,0.0000153167,0.00006672765,0.00005619653,0.00005982917],"domain_scores_gemma":[0.9997966,0.00006368518,0.00003210864,0.00002027436,0.00006445206,0.00002294745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004658658,0.0003162138,0.01262139,0.0002097676,0.0001963956,0.0003997295,0.000113391,0.2166867,0.01669847,0.004675355,0.01498183,0.7326349],"study_design_scores_gemma":[0.00001296589,0.0001096311,0.002091747,0.00003002019,0.00003291246,0.0001142239,0.00003011672,0.9867917,0.005766489,0.003140905,0.001868297,0.0000110869],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2833488,0.007778104,0.6948591,0.001643549,0.0002431899,0.0001582909,0.002098958,0.004848044,0.005021977],"genre_scores_gemma":[0.890381,0.001602718,0.100642,0.0004436792,0.00009719879,0.0001329575,0.002632468,0.00009664208,0.003971242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005869761,"threshold_uncertainty_score":0.01167119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09714611035614804,"score_gpt":0.3837437445253866,"score_spread":0.2865976341692386,"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."}}