{"id":"W4388915043","doi":"10.1016/j.brainresbull.2023.110825","title":"Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods","year":2023,"lang":"en","type":"article","venue":"Brain Research Bulletin","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"Korea Health Industry Development Institute; Ministry of Health and Welfare; Ministry of Science, ICT and Future Planning; Korea Dementia Research Center","keywords":"Hyperintensity; Fluid-attenuated inversion recovery; Segmentation; Artificial intelligence; Pattern recognition (psychology); Robustness (evolution); Computer science; Convolutional neural network; White matter; Magnetic resonance imaging; Medicine; Radiology; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005584096,0.0001465945,0.0003737641,0.0009841481,0.0001632496,0.00004432109,0.0001194448,0.0000448714,0.003020431],"category_scores_gemma":[0.0003466155,0.0001193459,0.00005944664,0.001189443,0.0002934683,0.00006828706,0.0001948546,0.0003723139,0.0006297574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001262683,"about_ca_system_score_gemma":0.0001821808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009513058,"about_ca_topic_score_gemma":0.00002483095,"domain_scores_codex":[0.9953071,0.00206014,0.0004289675,0.0003670659,0.001356608,0.0004800508],"domain_scores_gemma":[0.9978431,0.0009943858,0.00007938834,0.0002232142,0.0007480326,0.0001118728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001358991,0.001791363,0.9219062,0.000610335,0.0003410732,0.0002805158,0.01102679,0.00006268982,0.01809049,0.00006253901,0.04010105,0.004367969],"study_design_scores_gemma":[0.004384271,0.002107879,0.9381483,0.0003409889,0.00002130107,0.00001185925,0.04418733,0.002052017,0.007592118,0.00006376128,0.0009678244,0.0001223185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840178,0.00001701167,0.0004721273,0.009157895,0.00002330957,0.002345189,0.000009520431,0.0000498224,0.003907287],"genre_scores_gemma":[0.9884018,0.000007544188,0.003221332,0.0002157246,0.00002777893,0.0005866037,0.0001597006,0.00002220831,0.007357258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03913323,"threshold_uncertainty_score":0.9978909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1094235915911405,"score_gpt":0.4685053272609811,"score_spread":0.3590817356698406,"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."}}