{"id":"W4313591971","doi":"10.1016/j.compmedimag.2022.102171","title":"Towards better interpretable and generalizable AD detection using collective artificial intelligence","year":2023,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Centre National de la Recherche Scientifique; Eisai; Agence Nationale de la Recherche; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Interpretability; Artificial intelligence; Computer science; Machine learning; Deep learning; Generalizability theory; Neuroimaging; Convolutional neural network; Classifier (UML); Ensemble learning; Dementia; Graph; Disease; Medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003038513,0.001654682,0.001537918,0.002691893,0.0005414844,0.004172324,0.001620529,0.001956418,0.002503993],"category_scores_gemma":[0.01059963,0.0006719281,0.001851955,0.001498405,0.0009607972,0.002826113,0.002227008,0.002405108,0.001570059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006923348,"about_ca_system_score_gemma":0.0009173983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003152617,"about_ca_topic_score_gemma":0.003898104,"domain_scores_codex":[0.9981821,0.0006035713,0.0001548053,0.0006058568,0.0003458421,0.0001078855],"domain_scores_gemma":[0.9956101,0.001741314,0.0004008208,0.001252558,0.0008748388,0.0001204323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003983127,0.000440483,0.0140686,0.0006048289,0.0005561539,0.000442911,0.001155631,0.07741208,0.0552944,0.0204546,0.009494438,0.8196775],"study_design_scores_gemma":[0.00003725841,0.0002080388,0.007316606,0.000164794,0.0002238204,0.0002842339,0.000415002,0.8907453,0.01545518,0.07642469,0.00866761,0.00005745589],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04258378,0.0009320866,0.9466616,0.001086815,0.0001113076,0.0001738482,0.0004451469,0.00465019,0.00335515],"genre_scores_gemma":[0.2789111,0.0006408297,0.7151561,0.0005016359,0.000137784,0.0001395265,0.001374002,0.0004492615,0.002689701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004172324,"threshold_uncertainty_score":0.01606935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03706426654043213,"score_gpt":0.3408039305231152,"score_spread":0.303739663982683,"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."}}