{"id":"W2911963582","doi":"10.1155/2019/9507193","title":"Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening","year":2019,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Elan; Northern California Institute for Research and Education; Johns Hopkins University; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Merck; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Alzheimer's Drug Discovery Foundation; National Institute of Neurological Disorders and Stroke; IXICO; Takeda Pharmaceutical Company; AbbVie; Alzheimer's Association; Foundation for the National Institutes of Health; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics","keywords":"Dementia; Segmentation; Neuroimaging; Receiver operating characteristic; Magnetic resonance imaging; Cognitive decline; Cognition; Artificial intelligence; Computer science; Machine learning; Medicine; Radiology; Disease; Pathology; Psychiatry","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":[0.0007383142,0.001350715,0.001444455,0.001768049,0.000566378,0.001077812,0.001630636,0.0009579759,0.002663911],"category_scores_gemma":[0.002219132,0.0004091655,0.001117076,0.001293286,0.0002397056,0.0007884999,0.001381586,0.000761006,0.002091404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001280377,"about_ca_system_score_gemma":0.001205688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02106663,"about_ca_topic_score_gemma":0.02414908,"domain_scores_codex":[0.9995199,0.00004415456,0.0000469158,0.000216863,0.0001067064,0.00006532145],"domain_scores_gemma":[0.9993205,0.0001418013,0.000118048,0.0001095324,0.0001977441,0.0001123594],"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":[0.004155075,0.000765997,0.07753175,0.0006460672,0.0006547673,0.002985938,0.0006284716,0.1960462,0.0296015,0.004303212,0.1387506,0.5439304],"study_design_scores_gemma":[0.00006527576,0.00007433755,0.007423338,0.00003783994,0.00006441371,0.0002444126,0.00005791601,0.9801429,0.004944787,0.002640363,0.004263021,0.00004134313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1623037,0.002385592,0.7099887,0.002057391,0.000623275,0.001067418,0.03028137,0.08621815,0.005074447],"genre_scores_gemma":[0.6891682,0.001313076,0.2753811,0.0009395626,0.0003555928,0.0007009564,0.02797004,0.001028095,0.003143387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02106663,"threshold_uncertainty_score":0.04188806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300100579314469,"score_gpt":0.2983763816213805,"score_spread":0.2853753758282359,"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."}}