{"id":"W4206577272","doi":"10.1016/j.nicl.2022.102940","title":"Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects","year":2022,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Janssen Research and Development; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Francisco; Meso Scale Diagnostics; Johnson and Johnson; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Merck; Fujirebio Europe; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Fluid-attenuated inversion recovery; Hyperintensity; Multiple sclerosis; Segmentation; Dementia; White matter; Neuroimaging; CADASIL; Medicine; Cognition; Magnetic resonance imaging; Artificial intelligence; Computer science; Radiology; Disease; Pathology","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":[0.001306502,0.0001346405,0.0004965515,0.0001056666,0.0001876763,0.00001471986,0.0000766849,0.00002690788,0.00007024228],"category_scores_gemma":[0.0008557592,0.00009670608,0.00007196888,0.0001924468,0.0004293872,0.00006370078,0.0002911277,0.0003861015,0.000001683936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002705223,"about_ca_system_score_gemma":0.00004032179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002598258,"about_ca_topic_score_gemma":0.00001276901,"domain_scores_codex":[0.9975805,0.0007740917,0.000632219,0.0003340316,0.0005012538,0.0001779227],"domain_scores_gemma":[0.9973065,0.001874283,0.0002842612,0.0003205395,0.0001290278,0.00008540798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008954814,0.0003535178,0.9304048,0.0002603939,0.00007753778,0.000002944669,0.001304507,0.00005455674,0.03176904,8.711968e-7,0.001007025,0.03386931],"study_design_scores_gemma":[0.002523309,0.002421222,0.9740487,0.0001206618,0.00007328449,0.00001119164,0.0007403936,0.007991609,0.01187168,0.000004297114,0.0001147006,0.00007898233],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972155,0.00003306577,0.0008609171,0.0009229121,0.0001031087,0.0007101765,0.0000286896,0.0000216435,0.0001039687],"genre_scores_gemma":[0.9849429,0.00005658427,0.01426849,0.0005215831,0.00001563158,0.00003788816,0.00001001444,0.00002369095,0.0001232872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04364387,"threshold_uncertainty_score":0.394356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1369123166118126,"score_gpt":0.4050454498491978,"score_spread":0.2681331332373852,"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."}}