{"id":"W2981852549","doi":"10.1101/813899","title":"Detecting prodromal Alzheimer’s disease with MRI through deep learning","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"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; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; 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":"Dementia; Deep learning; Biomarker; Disease; Alzheimer's Disease Neuroimaging Initiative; Neuroscience; Medicine; Prodromal Stage; Psychology; Alzheimer's disease; Artificial intelligence; Pathology; Computer science; Biology","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":[0.0008059312,0.0007429786,0.0004483372,0.0006689923,0.0001412879,0.0005954913,0.0004000341,0.0004010561,0.0006779252],"category_scores_gemma":[0.001880311,0.0002773587,0.0005223812,0.0004414113,0.0003694371,0.0005924414,0.0006205767,0.0009131286,0.0002599489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004989219,"about_ca_system_score_gemma":0.0005489013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004032974,"about_ca_topic_score_gemma":0.005266153,"domain_scores_codex":[0.9998254,0.00005170189,0.00001241831,0.00005810045,0.00002659617,0.00002580051],"domain_scores_gemma":[0.999501,0.000206325,0.0001166502,0.00007354593,0.00005922535,0.00004327618],"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":[0.00162634,0.0009041172,0.1551275,0.0003298691,0.0008051204,0.0008474297,0.0002867311,0.4502428,0.1072132,0.004341272,0.004386166,0.2738895],"study_design_scores_gemma":[0.0000233574,0.0001751319,0.01976266,0.00002869422,0.00005988104,0.0001819783,0.00003111521,0.9560665,0.0161509,0.006788328,0.0007030346,0.000028421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7726724,0.001058306,0.2222373,0.000760155,0.0000690739,0.00005257767,0.0009208826,0.0009773144,0.001251923],"genre_scores_gemma":[0.9671931,0.0002072911,0.03144039,0.00009290712,0.00002419567,0.00002673758,0.0004906329,0.00001691216,0.0005078353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004032974,"threshold_uncertainty_score":0.00801897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01944891686851871,"score_gpt":0.2665604695553826,"score_spread":0.2471115526868639,"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."}}