{"id":"W2477263588","doi":"10.1101/066910","title":"Deep Learning-based Pipeline to Recognize Alzheimer’s Disease using fMRI Data","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Baycrest Hospital; McMaster University; University of Toronto","funders":"","keywords":"Pipeline (software); Disease; Neuroscience; Artificial intelligence; Psychology; Computer science; Medicine; Pattern recognition (psychology); Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008520833,0.0006519676,0.0004923034,0.0004457818,0.0004311202,0.0003891557,0.001755039,0.000340604,0.0002044797],"category_scores_gemma":[0.004183291,0.0006494689,0.0001444804,0.0008271407,0.0001793822,0.0003155289,0.001149065,0.0009100116,0.0005703789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002777503,"about_ca_system_score_gemma":0.0008291921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001558414,"about_ca_topic_score_gemma":0.000002312012,"domain_scores_codex":[0.9947845,0.000595163,0.0006926763,0.00252024,0.0006914014,0.0007160287],"domain_scores_gemma":[0.994385,0.0002721936,0.0006256476,0.003475688,0.0003647778,0.0008766875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001837677,0.000199056,0.0007608536,0.0001165863,0.00002680723,0.00007819178,0.000004123112,0.00141866,0.9961829,0.0001194243,0.0008451673,0.00006451723],"study_design_scores_gemma":[0.001017325,0.00006022536,0.01312564,0.0005813332,0.0003930206,4.917133e-8,0.000002094245,0.1862434,0.7447023,0.000007499365,0.05202497,0.001842121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3722191,0.001600175,0.5916286,0.01178391,0.01021095,0.005188541,0.002555247,0.004730229,0.00008330506],"genre_scores_gemma":[0.9940358,0.00005754657,0.002488024,0.002251081,0.0007785827,0.0001543721,0.000001580024,0.0002144411,0.00001855844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6218167,"threshold_uncertainty_score":0.9995956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08294678188801834,"score_gpt":0.287685498734956,"score_spread":0.2047387168469376,"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."}}