{"id":"W4379094958","doi":"10.1002/mp.16482","title":"Bone‐GAN: Generation of virtual bone microstructure of high resolution peripheral quantitative computed tomography","year":2023,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Bone and Joint Health Institute; University of Calgary","funders":"Consejo Nacional de Investigaciones Científicas y Técnicas; Salesforce","keywords":"Quantitative computed tomography; Computed tomography; Computed tomography laser mammography; Tomography; Materials science; Microstructure; High resolution; Peripheral; Medical imaging; Nuclear medicine; Medicine; Radiology; Biomedical engineering; Bone density; Preclinical imaging; Osteoporosis; Pathology; Geology","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":[0.0006613979,0.000568793,0.0002732872,0.0003391116,0.00008662256,0.0002936932,0.000640856,0.0004357126,0.00133324],"category_scores_gemma":[0.001183493,0.000296384,0.0005027144,0.0001853768,0.0004060758,0.0002153186,0.0005149281,0.0005176913,0.0003291947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003142279,"about_ca_system_score_gemma":0.0003036188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009971234,"about_ca_topic_score_gemma":0.00124522,"domain_scores_codex":[0.9998214,0.0000461637,0.000005253642,0.00004231628,0.00006739608,0.00001755178],"domain_scores_gemma":[0.9996868,0.0001442264,0.00003510857,0.000061823,0.00005314001,0.00001886644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002397885,0.000063745,0.001534197,0.0001329006,0.00007027509,0.0001648438,0.00006825171,0.8622036,0.05181846,0.002852156,0.002143783,0.07870807],"study_design_scores_gemma":[0.000006424047,0.00003199159,0.0003005892,0.000003895705,0.000004299287,0.00007307788,0.000002899518,0.9910672,0.007596492,0.0004990115,0.000409615,0.000004519084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06537567,0.0001661269,0.9307417,0.000106445,0.00004996646,0.00008142822,0.0002666585,0.001633505,0.001578578],"genre_scores_gemma":[0.7305267,0.000138729,0.2659428,0.0001297009,0.00002078945,0.0001475079,0.0007443218,0.0003284925,0.002020953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00133324,"threshold_uncertainty_score":0.004460096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564813206850581,"score_gpt":0.2463490311595652,"score_spread":0.2307008990910594,"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."}}