{"id":"W4295079170","doi":"10.1007/s12021-022-09602-6","title":"Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System","year":2022,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hotchkiss Brain Institute; Alberta Children's Hospital; Ontario Brain Institute; University of Calgary","funders":"","keywords":"Convolutional neural network; Neuroscience; Artificial intelligence; Deep learning; Cognition; Computer science; Neurodegeneration; Machine learning; Network model; Medicine; Psychology; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002343079,0.00009093433,0.0001873908,0.0001361245,0.0002113413,0.00002068209,0.00009555582,0.0000152821,0.000007790426],"category_scores_gemma":[0.0000366754,0.00007552244,0.00008982242,0.0005049118,0.00001994033,0.0001241318,0.0001503981,0.0003000013,8.451421e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007413769,"about_ca_system_score_gemma":0.00003443267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003322755,"about_ca_topic_score_gemma":0.00000243498,"domain_scores_codex":[0.9987634,0.0001268322,0.0004568601,0.00009184529,0.0003903565,0.0001707449],"domain_scores_gemma":[0.9995299,0.00003962348,0.0001760047,0.0001418747,0.00006468063,0.00004790421],"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":[0.00002974917,0.0000211834,0.0105474,0.00005681287,0.00001387636,0.000004460288,0.0003798357,0.9877865,0.0007179383,0.00009157805,0.00001101069,0.0003396567],"study_design_scores_gemma":[0.0003237197,0.00009245811,0.002350058,0.00003017216,0.0001035391,0.00006934739,0.0005877852,0.9962782,0.00006684678,0.000003083957,0.0000357178,0.00005911765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692957,0.00004783657,0.03001528,0.0001146144,0.0001537103,0.0001896905,0.000001591563,0.00002492777,0.0001566887],"genre_scores_gemma":[0.9990407,0.000002057305,0.0004650797,0.0003998114,0.00003508135,0.000004476459,0.00001208219,0.00001169139,0.0000290668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02974499,"threshold_uncertainty_score":0.3079716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02976596619390831,"score_gpt":0.2653925628715812,"score_spread":0.2356265966776729,"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."}}