{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000311445,0.0001339853,0.0004468116,0.0001780921,0.00008522473,0.00002539423,0.00006761553,0.000036251,0.00005248788],"category_scores_gemma":[0.0001153203,0.0001032128,0.0001326461,0.0001370165,0.00004385092,0.0001429238,0.00002921765,0.0001529635,8.925203e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005763194,"about_ca_system_score_gemma":0.000111449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001277169,"about_ca_topic_score_gemma":0.00002358162,"domain_scores_codex":[0.9989717,0.00008324002,0.0004603468,0.0001416523,0.0002067287,0.0001363149],"domain_scores_gemma":[0.9992704,0.0001002874,0.0002952795,0.000102819,0.0001564323,0.00007473225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007596851,0.002796221,0.2212425,0.0008627524,0.001353007,0.0001496473,0.004816329,0.00001888817,0.2082319,0.00001776082,0.0003402834,0.559411],"study_design_scores_gemma":[0.0255588,0.009548116,0.8575985,0.001579058,0.003574833,0.0001141628,0.02211265,0.001141781,0.07628506,0.0009767998,0.001148254,0.0003619482],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992043,0.001398605,0.004630975,0.000213506,0.00005601051,0.001382961,0.000004566817,0.00001331721,0.0002570347],"genre_scores_gemma":[0.9893616,0.00006281528,0.01019943,0.00002699535,0.00003534115,0.00007439222,0.00001037735,0.00001573532,0.0002132989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6363561,"threshold_uncertainty_score":0.4208896,"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."}}