{"id":"W4399879217","doi":"10.1101/2024.06.20.24309230","title":"segcsvd <sub>WMH</sub> : A convolutional neural network-based tool for quantifying white matter hyperintensities in heterogeneous patient cohorts","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; Western University; Centre for Addiction and Mental Health; University of Toronto; Sunnybrook Hospital","funders":"","keywords":"Hyperintensity; Convolutional neural network; White matter; Computer science; Artificial intelligence; Pattern recognition (psychology); Medicine; Magnetic resonance imaging; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001713564,0.001294326,0.0005201418,0.002262812,0.0004073158,0.0009168596,0.0009711818,0.0008833716,0.00215705],"category_scores_gemma":[0.004503732,0.0002850716,0.0006300855,0.0008224212,0.0003565962,0.0005026112,0.001558015,0.0007103396,0.0007397009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007632195,"about_ca_system_score_gemma":0.001098496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01142924,"about_ca_topic_score_gemma":0.01916195,"domain_scores_codex":[0.9995975,0.00007961196,0.00002940776,0.0001535446,0.00008470497,0.00005522186],"domain_scores_gemma":[0.9992761,0.0002906718,0.0001379318,0.000125995,0.0001086308,0.0000606848],"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":[0.001663,0.0004140692,0.1015422,0.0005307321,0.001645678,0.0009229292,0.0002750494,0.1583588,0.03525141,0.004456561,0.05345972,0.64148],"study_design_scores_gemma":[0.0001112282,0.0002009772,0.03147888,0.00007599785,0.0001660661,0.0006701685,0.00007793108,0.9263889,0.02528517,0.006096073,0.009380626,0.00006801641],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5175222,0.002833494,0.4226245,0.001326502,0.0002601703,0.0005256261,0.02390587,0.02540857,0.005593081],"genre_scores_gemma":[0.7914776,0.0004914618,0.1824744,0.0005077453,0.00008832542,0.0003924576,0.01992059,0.0007128775,0.003934419],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01142924,"threshold_uncertainty_score":0.02272546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05201676108527119,"score_gpt":0.2673684485994956,"score_spread":0.2153516875142244,"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."}}