{"id":"W4408089719","doi":"10.1002/jcsm.13728","title":"Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study","year":2025,"lang":"en","type":"article","venue":"Journal of Cachexia Sarcopenia and Muscle","topic":"Hip disorders and treatments","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"National Center for Advancing Translational Sciences; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute on Aging; National Institutes of Health","keywords":"Medicine; Hounsfield scale; Quantitative computed tomography; Sarcopenia; Adipose tissue; Sørensen–Dice coefficient; Pelvis; Thigh; Osteoporosis; Nuclear medicine; Radiology; Bone mineral; Segmentation; Anatomy; Computed tomography; Image segmentation; Pathology; Internal medicine; Artificial intelligence","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":[0.001545488,0.0007006874,0.0005700964,0.001080416,0.000227781,0.0005561453,0.0007196272,0.001027413,0.0006765085],"category_scores_gemma":[0.002841814,0.0003654443,0.0007176973,0.0005265009,0.000291234,0.0003378676,0.000616429,0.0005375546,0.0002868907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004655438,"about_ca_system_score_gemma":0.0006255613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007059428,"about_ca_topic_score_gemma":0.005862242,"domain_scores_codex":[0.9996092,0.0001206383,0.0000351561,0.000109824,0.0000753644,0.00004995069],"domain_scores_gemma":[0.9991474,0.0003429824,0.0001197322,0.00007319362,0.0002692896,0.00004743969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001685629,0.0008139467,0.0670641,0.0004171634,0.000660559,0.0008652209,0.0003612519,0.3500449,0.08229946,0.001265755,0.003783845,0.4907382],"study_design_scores_gemma":[0.00002162231,0.0001829652,0.009144156,0.00002099767,0.00005222525,0.0002057355,0.00003829545,0.9800206,0.00948077,0.0003611946,0.0004563531,0.00001512281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6857419,0.001120962,0.310102,0.0003262539,0.00005031766,0.0001570829,0.0004936428,0.001224996,0.0007827717],"genre_scores_gemma":[0.8953928,0.0002895171,0.1023058,0.000149474,0.00002617796,0.0001019782,0.0007108068,0.0000670714,0.0009563961],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007059428,"threshold_uncertainty_score":0.01403666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056769605020675,"score_gpt":0.3192380716177727,"score_spread":0.3086703755675659,"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."}}