{"id":"W2954208501","doi":"10.1007/s11548-019-02020-1","title":"Toward real-time rigid registration of intra-operative ultrasound with preoperative CT images for lumbar spinal fusion surgery","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Medicine; Image-guided surgery; Lumbosacral joint; Vertebra; Spinal fusion; Radiology; Lumbar; Image registration; Lumbar vertebrae; Computer science; Artificial intelligence; Surgery; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006798935,0.0001460187,0.0005668741,0.0002850525,0.00003058379,0.00004956157,0.0001199046,0.00005559428,0.00005166641],"category_scores_gemma":[0.0001026412,0.0001085353,0.000187631,0.00008835278,0.0001268091,0.0002437631,0.00001064494,0.0001725289,0.000001828289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004130985,"about_ca_system_score_gemma":0.0001034679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007391957,"about_ca_topic_score_gemma":5.922462e-7,"domain_scores_codex":[0.99873,0.0001259861,0.0006241703,0.0001418273,0.0002409694,0.0001370383],"domain_scores_gemma":[0.9979893,0.001131078,0.0002910577,0.00007749134,0.0004357076,0.00007537656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002399578,0.0006929052,0.5005333,0.0008444066,0.01347997,0.001348508,0.002152361,0.03772398,0.1727848,0.0008594564,0.129485,0.1376957],"study_design_scores_gemma":[0.004758731,0.001585656,0.7954349,0.004157996,0.0008050579,0.02114747,0.0004540677,0.1331683,0.03140134,0.0007231553,0.004640296,0.001723072],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.941503,0.0002635976,0.05671536,0.0004632676,0.0007731259,0.00007348244,0.00001488716,0.00001961109,0.0001736458],"genre_scores_gemma":[0.9952381,0.0006405414,0.003563807,0.00007576827,0.0003670777,0.000003126008,0.00004323198,0.00001196307,0.00005636602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2949015,"threshold_uncertainty_score":0.4425939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01190651338420715,"score_gpt":0.2498102437120892,"score_spread":0.2379037303278821,"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."}}