{"id":"W4390665001","doi":"10.3389/fnagi.2023.1303036","title":"A deep learning model for brain age prediction using minimally preprocessed T1w images as input","year":2024,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; Parkinsonfonden; National Institutes of Health; IXICO; H. Lundbeck A/S; Mitsubishi Tanabe Pharma Corporation; Servier; Shionogi; Japan Science and Technology Agency; Vetenskapsrådet; Eisai; Astellas Pharma; Karolinska Institutet; Center for Innovative Medicine; Northern California Institute for Research and Education; Daiichi-Sankyo; Universidad de La Laguna; European Commission; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Genentech; Kungliga Tekniska Högskolan; Nvidia; F. Hoffmann-La Roche; University of Southern California; Stockholms Läns Landsting; Biogen; Pfizer; BioClinica; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Åke Wiberg Stiftelse","keywords":"Deep learning; Artificial intelligence; Computer science; Psychology; Machine learning; Neuroscience","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":[0.0007497518,0.001015593,0.0005332166,0.0005473514,0.0002703051,0.0005081677,0.001178368,0.0009625441,0.001771334],"category_scores_gemma":[0.001412473,0.0003948343,0.0007141083,0.0004289089,0.0002614978,0.0006248212,0.0005132398,0.001059167,0.0007769794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038417,"about_ca_system_score_gemma":0.0009221429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01748184,"about_ca_topic_score_gemma":0.01395015,"domain_scores_codex":[0.9998366,0.00002295108,0.000009819645,0.00007034741,0.00002668661,0.00003369336],"domain_scores_gemma":[0.9996464,0.0001179803,0.00004079505,0.00002507918,0.0001532694,0.00001648722],"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":[0.0003041558,0.000215844,0.007994642,0.0001011394,0.0001703223,0.0001634231,0.00005187953,0.7708871,0.006926134,0.001243858,0.005397335,0.2065442],"study_design_scores_gemma":[0.000004700047,0.00002689094,0.0005230109,0.00000882554,0.00001537679,0.00001521067,0.000002725821,0.9973918,0.001247476,0.0004561473,0.0003029946,0.00000475843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2836083,0.003480851,0.6993761,0.001445511,0.0003963515,0.0001687789,0.002510738,0.004333109,0.004680268],"genre_scores_gemma":[0.9042609,0.0008674675,0.08323373,0.0004592815,0.00009696196,0.0003053953,0.002775087,0.00009736281,0.007903887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01748184,"threshold_uncertainty_score":0.03476018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03995335792602721,"score_gpt":0.2922462286899727,"score_spread":0.2522928707639455,"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."}}