{"id":"W4362703920","doi":"10.1016/j.jbi.2023.104357","title":"Deep learning prediction of motor performance in stroke individuals using neuroimaging data","year":2023,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; Centre Intégré de Santé et de Services Sociaux des Laurentides; Jewish Rehabilitation Hospital; McGill University","funders":"Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Université de Genève; University of British Columbia; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; UCL Institute of Neurology, University College London; Canada Foundation for Innovation","keywords":"Artificial intelligence; Fractional anisotropy; Support vector machine; Diffusion MRI; Convolutional neural network; Neuroimaging; Computer science; Machine learning; Naive Bayes classifier; Pattern recognition (psychology); Cross-validation; Population; Magnetic resonance imaging; Medicine; Psychology; Neuroscience; 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":[0.00100931,0.0006167646,0.0004899956,0.0006472474,0.0001208549,0.0004413564,0.0003168913,0.0005415762,0.0006118987],"category_scores_gemma":[0.00250703,0.0001473512,0.0004678199,0.0003198314,0.0001460867,0.000414749,0.0003796091,0.0005878575,0.0002969798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004024511,"about_ca_system_score_gemma":0.0005028642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007257015,"about_ca_topic_score_gemma":0.007731564,"domain_scores_codex":[0.9997937,0.00005254487,0.00001945737,0.00006299512,0.00002483074,0.00004653029],"domain_scores_gemma":[0.9993933,0.0002949512,0.00008403367,0.00004515835,0.0001350111,0.00004755811],"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":[0.001071132,0.000914857,0.2869369,0.0001310184,0.0003891073,0.0003156895,0.0001361706,0.4229954,0.009660493,0.0005620045,0.002910567,0.2739767],"study_design_scores_gemma":[0.000008557289,0.0001422051,0.02550104,0.0000191258,0.00002157207,0.00004451748,0.00002626827,0.9716904,0.00176871,0.0006151535,0.0001529331,0.000009486972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527149,0.0005593033,0.04461375,0.0002194572,0.00004167254,0.00005191194,0.0008171839,0.0002829277,0.0006989331],"genre_scores_gemma":[0.9923263,0.0001291628,0.006321543,0.00003679293,0.000009205605,0.00003030467,0.0006739164,0.000005284989,0.000467499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007257015,"threshold_uncertainty_score":0.01442957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0531152743285396,"score_gpt":0.3218665079062785,"score_spread":0.2687512335777389,"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."}}