{"id":"W2586054413","doi":"10.1117/12.2255907","title":"3D point cloud analysis of structured light registration in computer-assisted navigation in spinal surgeries","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spinal Fractures and Fixation Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Point cloud; Navigation system; Computer vision; Image registration; Computer science; Artificial intelligence; Robustness (evolution); Sagittal plane; Point (geometry); Image-guided surgery; Patient registration; Medicine; Mathematics; Radiology; Image (mathematics)","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":[0.003190424,0.0004543499,0.000451929,0.005129354,0.0004212352,0.001185601,0.0006258425,0.0005610179,0.0008502456],"category_scores_gemma":[0.009237943,0.0004335121,0.0007467342,0.003632861,0.0007515574,0.00091022,0.0007630301,0.0004401143,0.0005222798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000654482,"about_ca_system_score_gemma":0.001180281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004425148,"about_ca_topic_score_gemma":0.006472971,"domain_scores_codex":[0.9965025,0.0009076403,0.0002829851,0.0002294797,0.001990675,0.00008662387],"domain_scores_gemma":[0.9926238,0.003301135,0.001023839,0.0007524334,0.002208953,0.00008991155],"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":[0.001150158,0.0003125556,0.125956,0.001471681,0.0003585892,0.001326182,0.001298097,0.05148678,0.1231612,0.003492304,0.001906039,0.6880804],"study_design_scores_gemma":[0.00007284002,0.001178834,0.3120322,0.0003931203,0.0004957294,0.006392856,0.001102552,0.5004714,0.1622501,0.005416641,0.009885629,0.0003080764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3945243,0.004264571,0.5958571,0.0001668689,0.00008791829,0.0005970123,0.0007767643,0.0007500329,0.002975378],"genre_scores_gemma":[0.8499212,0.001853572,0.1469809,0.00003060319,0.00003185976,0.0001389775,0.0004859574,0.0001056314,0.0004513528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005129354,"threshold_uncertainty_score":0.01687282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419219782213171,"score_gpt":0.2720649622879114,"score_spread":0.2578727644657797,"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."}}