{"id":"W4413572014","doi":"10.1093/braincomms/fcaf319","title":"Improving diagnostic accuracy of routine EEG for epilepsy using deep learning","year":2025,"lang":"en","type":"article","venue":"Brain Communications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Eisai; Union Chimique Belge; Universidade Católica de Brasília; Canada Research Chairs; Natural Sciences and Engineering Research Council of Canada; Eisai Canada; Institut de Valorisation des Données; Fondation Brain Canada","keywords":"Epilepsy; Electroencephalography; Computer science; Artificial intelligence; Deep learning; Audiology; Medicine; Pattern recognition (psychology); Psychology; Neuroscience","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":[0.00378923,0.0007853666,0.000555191,0.00155161,0.0001623437,0.001037871,0.0005368456,0.0004875054,0.0005901316],"category_scores_gemma":[0.01556765,0.0002631681,0.0005657654,0.000574101,0.000417491,0.001146321,0.0009596331,0.0007443524,0.0002522444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000448707,"about_ca_system_score_gemma":0.0005143883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002238763,"about_ca_topic_score_gemma":0.003972266,"domain_scores_codex":[0.9985402,0.0006018734,0.0001911055,0.0002915384,0.0002518072,0.0001236611],"domain_scores_gemma":[0.9932951,0.004225539,0.0009181055,0.0005064788,0.0008742296,0.0001804683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001122009,0.0003559225,0.6768509,0.0002157739,0.0005628907,0.0004885768,0.0002734141,0.03460908,0.01078253,0.0004833054,0.001813854,0.2724417],"study_design_scores_gemma":[0.00009905296,0.001050911,0.1990714,0.0001619635,0.0003051465,0.001391151,0.0002301421,0.7784091,0.0149899,0.002928835,0.001276621,0.00008576391],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9475076,0.001211508,0.04889196,0.0004506918,0.00004866617,0.00004412812,0.0004101721,0.0004027055,0.001032461],"genre_scores_gemma":[0.991923,0.0001788524,0.007446673,0.00005237375,0.00001675938,0.000008981292,0.0002589458,0.00001082524,0.0001034515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00378923,"threshold_uncertainty_score":0.02003962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05471148760996429,"score_gpt":0.3518749621912409,"score_spread":0.2971634745812766,"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."}}