{"id":"W4386074683","doi":"10.11159/mvml23.001","title":"The importance of Integration of AI, Brain Neuro-Imaging Machine Vision, Peripheral Blood Gene Expressions, and Genomics for Better Prognosis and Diagnosis Predictions of Alzheimer's Disease","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Neuroimaging; Disease; Genomics; Peripheral blood; Neuroscience; Computer science; Peripheral; Medicine; Artificial intelligence; Gene; Pathology; Genome; Biology; Internal medicine; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002512906,0.0008117703,0.001231653,0.001962942,0.0002338603,0.002394722,0.0004816309,0.0009233139,0.001067382],"category_scores_gemma":[0.003757087,0.0003216018,0.000785493,0.001387583,0.0005223662,0.002239608,0.0007360342,0.00150011,0.000680808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004602833,"about_ca_system_score_gemma":0.000694452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002015169,"about_ca_topic_score_gemma":0.002073545,"domain_scores_codex":[0.9988533,0.0004067754,0.0001077875,0.0002340015,0.000309788,0.00008835401],"domain_scores_gemma":[0.9977793,0.001149677,0.0001670427,0.0002251654,0.0005791893,0.00009970638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000534117,0.0003797042,0.0341592,0.0005386753,0.0004209059,0.0002755752,0.000187385,0.02756838,0.0540739,0.008755576,0.01036695,0.8627396],"study_design_scores_gemma":[0.00007735307,0.0007151088,0.1314484,0.0006126919,0.0009875242,0.001182582,0.0007516113,0.6721091,0.06513774,0.08152409,0.0451101,0.0003437175],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2148021,0.04926737,0.6875098,0.02441045,0.001303649,0.0002771832,0.002474846,0.003975146,0.01597945],"genre_scores_gemma":[0.6151744,0.02786681,0.3453521,0.002786413,0.002360408,0.0001392995,0.001922498,0.0002491458,0.004148939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002512906,"threshold_uncertainty_score":0.01328963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01247452275085025,"score_gpt":0.2383570339306114,"score_spread":0.2258825111797612,"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."}}