{"id":"W4223611754","doi":"10.1016/j.neuroscience.2022.03.026","title":"A Single Model Deep Learning Approach for Alzheimer’s Disease Diagnosis","year":2022,"lang":"en","type":"article","venue":"Neuroscience","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institute on Aging; Chinese Academy of Sciences","keywords":"Overfitting; Artificial intelligence; Computer science; Convolutional neural network; Deep learning; Machine learning; Cognitive impairment; Classifier (UML); Pattern recognition (psychology); Cognition; Artificial neural network; Neuroscience; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005273516,0.0001580295,0.000140602,0.000139053,0.001509386,0.0002032133,0.001949705,0.0000192426,0.000007339865],"category_scores_gemma":[0.0006087078,0.0001700533,0.00009296037,0.0007967064,0.00008061358,0.0004031292,0.001081742,0.0004440111,0.000002147181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006936842,"about_ca_system_score_gemma":0.0001476559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001669607,"about_ca_topic_score_gemma":7.185051e-7,"domain_scores_codex":[0.9973595,0.0002524735,0.0002099235,0.0009575017,0.000674464,0.0005462026],"domain_scores_gemma":[0.9987289,0.0001891555,0.0001338913,0.0006068618,0.00005619532,0.0002850282],"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":[0.000009346158,0.0001665927,0.01183348,0.00001840454,7.50426e-7,0.000008241146,0.0004580651,0.9565864,0.0002059897,0.007371488,0.0002027512,0.02313852],"study_design_scores_gemma":[0.0001286536,0.000239094,0.002523263,0.000001878831,0.000006492231,0.000009692611,0.00001626788,0.9875164,0.00004015601,0.0006535105,0.008680308,0.0001842755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01082054,0.0003701841,0.98452,0.002212974,0.0005364097,0.0005837053,0.00001010006,0.0004319956,0.0005141156],"genre_scores_gemma":[0.9395396,0.000007455075,0.05642016,0.002698173,0.00003797228,0.001052048,0.000004999204,0.00002142248,0.0002182442],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.928719,"threshold_uncertainty_score":0.9997905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08283330906699173,"score_gpt":0.3096003890032547,"score_spread":0.2267670799362629,"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."}}