{"id":"W2913074424","doi":"10.3389/fneur.2019.00007","title":"Automated Generation of Radiologic Descriptions on Brain Volume Changes From T1-Weighted MR Images: Initial Assessment of Feasibility","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Johns Hopkins University; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; National Institute on Aging; Alzheimer's Association","keywords":"Nuclear medicine; Medicine; Volume (thermodynamics); Neuroimaging; Radiology; Artificial intelligence; Medical physics; Computer science; Neuroscience; Psychology; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004190058,0.0001358234,0.000557185,0.0002758304,0.00002382592,0.00000476579,0.0001215453,0.0001721279,0.0001231244],"category_scores_gemma":[0.0003940575,0.0001206299,0.00006750599,0.0001863951,0.0002032035,0.00003781877,0.00003623569,0.0004970839,0.000002919677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005961179,"about_ca_system_score_gemma":0.00008748799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001441979,"about_ca_topic_score_gemma":0.000009585496,"domain_scores_codex":[0.9984138,0.0004569771,0.0003717889,0.0003635328,0.0001797711,0.000214144],"domain_scores_gemma":[0.9991856,0.0001667844,0.0001950241,0.00032778,0.00006057142,0.0000643025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003356109,0.0003188935,0.8582282,0.0000556865,0.00006382118,0.00004267714,0.0001578802,0.0007025924,0.1127754,0.00007143299,0.02446385,0.002783888],"study_design_scores_gemma":[0.002063484,0.001488933,0.4679741,0.00002715218,0.00003562698,0.00001217281,0.00002478354,0.5263722,0.00123003,0.0001960586,0.0004968272,0.00007862809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835681,0.0001133726,0.007512882,0.006253412,0.001526766,0.0004875099,0.00003004534,0.00007621093,0.0004316847],"genre_scores_gemma":[0.9839867,0.00004243321,0.01305329,0.002498559,0.0001359578,0.00001493014,0.000183861,0.00001702984,0.00006728117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5256696,"threshold_uncertainty_score":0.4919145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02707148432648494,"score_gpt":0.3210304032517301,"score_spread":0.2939589189252452,"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."}}