{"id":"W4322491365","doi":"10.1038/s43856-023-00262-4","title":"MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer’s disease, and healthy controls","year":2023,"lang":"en","type":"article","venue":"Communications Medicine","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; National Institute of Mental Health; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Temporal lobe; Epilepsy; Neuroscience; Disease; Psychology; Medicine; Artificial intelligence; Pathology; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002470588,0.0006867367,0.0003351516,0.0008787384,0.0001904395,0.000607226,0.0003148036,0.0006952613,0.0009107739],"category_scores_gemma":[0.005800084,0.0001623959,0.0004155942,0.0002134329,0.0003112817,0.0005805926,0.0004754245,0.0004969614,0.0002399768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006207408,"about_ca_system_score_gemma":0.0003139509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006324126,"about_ca_topic_score_gemma":0.007985041,"domain_scores_codex":[0.9994331,0.0001629987,0.00006379167,0.0001734755,0.00008653183,0.00008018226],"domain_scores_gemma":[0.9978224,0.001180133,0.0003391028,0.0002032983,0.0002684328,0.0001865582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004965635,0.0008005581,0.758219,0.0001462699,0.0008194373,0.0003045646,0.0002172208,0.01497814,0.01564609,0.0004111593,0.002827448,0.2006645],"study_design_scores_gemma":[0.0002440165,0.001593645,0.7292353,0.0001215629,0.0005398889,0.0009311178,0.0002460177,0.2481814,0.01396148,0.003095499,0.001773679,0.00007637102],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939989,0.0006643417,0.003629039,0.0002504175,0.00003730048,0.0000246332,0.0003307353,0.0001023874,0.0009622036],"genre_scores_gemma":[0.9976597,0.00009957157,0.00142007,0.00007459764,0.00001558276,0.000009376072,0.0004393695,0.000005456116,0.0002763343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006324126,"threshold_uncertainty_score":0.01306587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.108914061947099,"score_gpt":0.3943310425602808,"score_spread":0.2854169806131819,"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."}}