{"id":"W3095441110","doi":"10.3389/fneur.2020.576194","title":"Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease","year":2020,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Janssen Alzheimer Immunotherapy Research And Development; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; F. Hoffmann-La Roche; Biogen; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Disease; Neuroscience; Transfer of learning; Medicine; Artificial intelligence; Psychology; Machine learning; Computer science; Pathology","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":[0.003880196,0.001306168,0.0009153041,0.001375178,0.0002156032,0.0005177023,0.0008530745,0.0008566027,0.0004633252],"category_scores_gemma":[0.005510392,0.0001725173,0.0006046701,0.0005406922,0.0004651266,0.001227522,0.001195153,0.0008269547,0.0002565494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004981426,"about_ca_system_score_gemma":0.0005226043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002217506,"about_ca_topic_score_gemma":0.001814901,"domain_scores_codex":[0.9988004,0.0004353083,0.00006870057,0.0002645395,0.0002892829,0.0001417642],"domain_scores_gemma":[0.9982544,0.000820761,0.0001348208,0.0002533895,0.000406992,0.0001296342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003541859,0.001030371,0.06957672,0.000387141,0.001490345,0.0003424147,0.0002172363,0.3495753,0.01671258,0.001077282,0.003188965,0.5528598],"study_design_scores_gemma":[0.00006231689,0.001685276,0.02345017,0.00003590622,0.0002280757,0.0001803213,0.00007253359,0.9626147,0.009558447,0.001405697,0.0006718783,0.00003480426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9274743,0.003483052,0.06456112,0.0003547758,0.0002211588,0.0001289998,0.000319624,0.00103992,0.002417017],"genre_scores_gemma":[0.9884434,0.0004353163,0.009948703,0.0000832432,0.00006696598,0.00003840764,0.0004345589,0.00002606426,0.0005233729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003880196,"threshold_uncertainty_score":0.02052075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03842687255133427,"score_gpt":0.2874083135200718,"score_spread":0.2489814409687375,"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."}}