{"id":"W3217170481","doi":"10.1101/2021.11.27.470184","title":"OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Calgary","funders":"","keywords":"Neuroimaging; Artificial intelligence; Computer science; Transformer; Dementia; Resting state fMRI; Deep learning; Functional magnetic resonance imaging; Machine learning; Cognition; Architecture; Pattern recognition (psychology); Neuroscience; Disease; Psychology; Medicine; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005650451,0.001360044,0.0006029541,0.0005793943,0.0002634128,0.0007748057,0.001634186,0.0009966058,0.002157761],"category_scores_gemma":[0.001536107,0.0004908993,0.001134332,0.0002963321,0.0003333449,0.0008949776,0.0008302693,0.001575132,0.0009597638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067595,"about_ca_system_score_gemma":0.001274896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01686946,"about_ca_topic_score_gemma":0.02218213,"domain_scores_codex":[0.9998437,0.00002227487,0.000007731554,0.00006447994,0.00002765562,0.00003415465],"domain_scores_gemma":[0.9998142,0.00008061252,0.0000141347,0.00002174199,0.00004974708,0.00001957898],"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":[0.0006441702,0.0002741703,0.005225306,0.0001695633,0.0002783938,0.0002541626,0.00008683883,0.6136493,0.01563963,0.003263732,0.01507234,0.3454424],"study_design_scores_gemma":[0.00002252205,0.00005220103,0.0002382293,0.000007927046,0.00002135275,0.00004620567,0.000005558782,0.9943442,0.003219465,0.001315933,0.0007174895,0.000008927036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1478885,0.002185805,0.8146611,0.0009119755,0.0004154796,0.0002515416,0.001540263,0.02698284,0.005162376],"genre_scores_gemma":[0.821956,0.000488857,0.1648133,0.00077696,0.00005895445,0.0001895252,0.003074742,0.0006503859,0.007991385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01686946,"threshold_uncertainty_score":0.03354251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0420759673928241,"score_gpt":0.2797606208589782,"score_spread":0.2376846534661541,"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."}}