{"id":"W2594365872","doi":"10.1117/12.2256024","title":"Visualization of scoliotic spine using ultrasound-accessible skeletal landmarks","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Scoliosis; Computer science; Visualization; Computer vision; Artificial intelligence; Ground truth; Interpolation (computer graphics); Landmark; Spline (mechanical); Medicine; Physics; Image (mathematics); Surgery","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":[0.0004086044,0.0005238496,0.0002473168,0.0008586142,0.0001343625,0.0007370475,0.0003082437,0.0004802885,0.00359605],"category_scores_gemma":[0.001338043,0.000285276,0.0003892137,0.0003125836,0.000397624,0.0004252132,0.0006373015,0.0005470898,0.0006673203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001727418,"about_ca_system_score_gemma":0.0005833997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008601487,"about_ca_topic_score_gemma":0.0008913378,"domain_scores_codex":[0.9997674,0.00004790644,0.0000154201,0.0000364355,0.0001166917,0.00001619164],"domain_scores_gemma":[0.9995583,0.0001954975,0.00005600183,0.00006500578,0.00008107407,0.00004422283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003410623,0.00007536248,0.009044771,0.0006375122,0.00005528812,0.001249724,0.0006057057,0.02688591,0.8302178,0.001968824,0.002235643,0.1266823],"study_design_scores_gemma":[0.0001785739,0.001451786,0.05819483,0.0003699333,0.0001537634,0.02382534,0.0004554446,0.2517996,0.6285391,0.003819584,0.03096296,0.0002490512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3875456,0.002172541,0.6022713,0.0005409606,0.00007872503,0.0001939927,0.0007316783,0.00298479,0.003480445],"genre_scores_gemma":[0.7580653,0.0009685289,0.2377734,0.0001104174,0.00004973817,0.0001120613,0.0006737405,0.0004484644,0.001798465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00359605,"threshold_uncertainty_score":0.01202995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01385017846720904,"score_gpt":0.2663190882170688,"score_spread":0.2524689097498598,"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."}}