{"id":"W4413109010","doi":"10.1038/s41467-025-62590-4","title":"AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Avid Radiopharmaceuticals; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute on Aging; Commonwealth Scientific and Industrial Research Organisation; Northern California Institute for Research and Education; University of Southern California; Pfizer; BioClinica; Biogen; Brigham and Women's Hospital; National Heart, Lung, and Blood Institute; Novartis Pharmaceuticals Corporation; Eli Lilly and Company; Bristol-Myers Squibb; National Center for Advancing Translational Sciences; Meso Scale Diagnostics; Alzheimer's Association; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; GHR Foundation; American Heart Association; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Biomarker; Limiting; Disease; Neuroimaging; Alzheimer's disease; Clinical trial; Modalities; Medicine; Pet imaging; Computer science; Neuroscience; Bioinformatics; Pathology; Positron emission tomography; Artificial intelligence; Psychology; Biology; Nuclear medicine","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":[0.003290585,0.001347782,0.001560206,0.001716586,0.000513742,0.001491728,0.001591618,0.001060351,0.001739082],"category_scores_gemma":[0.007404885,0.0004637842,0.001697275,0.001522701,0.0005301948,0.001370134,0.002013665,0.001582845,0.0005521086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008969452,"about_ca_system_score_gemma":0.001288929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00892944,"about_ca_topic_score_gemma":0.01012894,"domain_scores_codex":[0.9991217,0.0003184032,0.00005475841,0.0002699491,0.0001452074,0.00008997408],"domain_scores_gemma":[0.9981838,0.0009807687,0.0001757925,0.0002063757,0.0003245683,0.0001286583],"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":[0.000928993,0.0005185902,0.02482705,0.0002515319,0.0008262419,0.000512178,0.0002518283,0.7568798,0.01023782,0.005820368,0.005778416,0.1931672],"study_design_scores_gemma":[0.00001046882,0.00004860904,0.001120758,0.000007761731,0.00004039728,0.00003588673,0.00001705957,0.9921014,0.0009172103,0.005145108,0.0005389323,0.00001648772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1462772,0.001342425,0.8416102,0.001259277,0.0001603247,0.0002171171,0.002916879,0.003690036,0.002526595],"genre_scores_gemma":[0.8641863,0.0003910883,0.1289376,0.0004948796,0.0001756589,0.0002460253,0.004018557,0.0001804492,0.001369462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00892944,"threshold_uncertainty_score":0.01775491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08066981206924997,"score_gpt":0.4771441706318224,"score_spread":0.3964743585625724,"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."}}