{"id":"W4309334867","doi":"10.1016/j.inffus.2022.11.015","title":"Information loss challenges in surgical navigation systems: From information fusion to AI-based approaches","year":2022,"lang":"en","type":"article","venue":"Information Fusion","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"National Key Research and Development Program of China; Shenyang Science and Technology Bureau; Barts and The London School of Medicine and Dentistry; National Natural Science Foundation of China; Ministry of Education - Singapore; Ministry of Science and Technology of the People's Republic of China; Ministry of Education of the People's Republic of China; Fundamental Research Funds for the Central Universities; Queen Mary, University of London","keywords":"Computer science; Modalities; Surgical planning; Navigation system; Modality (human–computer interaction); Tracking system; Artificial intelligence; Medicine; Surgery; Kalman filter","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":[0.005012115,0.001021139,0.001875359,0.001707667,0.0007138597,0.004240033,0.001705748,0.002281686,0.00117994],"category_scores_gemma":[0.01286924,0.000614611,0.0007443626,0.001995657,0.002459323,0.006161387,0.003142059,0.003068258,0.0003980933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001052369,"about_ca_system_score_gemma":0.0009321327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002902036,"about_ca_topic_score_gemma":0.001389219,"domain_scores_codex":[0.9973706,0.0007034226,0.0001750134,0.0003072993,0.001297725,0.0001458935],"domain_scores_gemma":[0.9938763,0.003836827,0.0005532505,0.0004395556,0.001159184,0.0001348438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002720876,0.0001215161,0.003165798,0.0009985303,0.0003685867,0.0003020145,0.000474249,0.3229403,0.006030926,0.08999904,0.006872056,0.568455],"study_design_scores_gemma":[0.00001895871,0.0001370203,0.002173157,0.0002107444,0.00009585408,0.0004367444,0.0004297298,0.7858499,0.004019132,0.1990175,0.00752831,0.00008292228],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01379774,0.02384529,0.9490007,0.008118289,0.0004446792,0.00003288411,0.0001325918,0.0002091134,0.004418762],"genre_scores_gemma":[0.8366811,0.03264722,0.1230495,0.001485681,0.002264136,0.00009871455,0.0002753583,0.0001441635,0.003354167],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005012115,"threshold_uncertainty_score":0.0265069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03877635032385154,"score_gpt":0.2441814556296228,"score_spread":0.2054051053057713,"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."}}