{"id":"W4392506624","doi":"10.3390/diagnostics14050567","title":"Enhancing Single-Plane Fluoroscopy: A Self-Calibrating Bundle Adjustment for Distortion Modeling","year":2024,"lang":"en","type":"article","venue":"Diagnostics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Fluoroscopy; Calibration; Ghosting; Computer science; Distortion (music); Artificial intelligence; Computer vision; Bundle adjustment; Image quality; Reliability (semiconductor); Image (mathematics); Mathematics; Physics; Medicine; Radiology","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":[0.0001562731,0.0001197321,0.000170157,0.00005782573,0.00008267332,0.00005059311,0.00005464084,0.00007345511,0.00003041838],"category_scores_gemma":[0.0004756842,0.0001063046,0.00007042681,0.0001269187,0.00002018948,0.00006019039,0.00002814648,0.0001532582,0.00001922049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001280575,"about_ca_system_score_gemma":0.00008098327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001722986,"about_ca_topic_score_gemma":0.000003273212,"domain_scores_codex":[0.9990512,0.000009095019,0.0002881227,0.0002561754,0.0001751635,0.0002202528],"domain_scores_gemma":[0.999166,0.0004096993,0.00003119383,0.0001919378,0.00006120206,0.0001399606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001439353,0.006480037,0.001970606,0.01100701,0.0006535196,0.0002031202,0.005040769,0.001016095,0.5028221,0.07101978,0.2588248,0.1408182],"study_design_scores_gemma":[0.0003596247,0.0004623572,0.00002448851,0.001613073,0.0004299235,0.00003985261,0.0001004148,0.8769167,0.07645949,0.002249877,0.04110594,0.0002382281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06042765,0.001777951,0.9319997,0.003159438,0.0003673662,0.0008657323,0.00004283927,0.001004853,0.0003544868],"genre_scores_gemma":[0.7510723,0.0004348319,0.2460366,0.000716533,0.0007835142,0.0004387902,0.0003188901,0.00004966828,0.0001489019],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8759006,"threshold_uncertainty_score":0.4334977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02958327811840416,"score_gpt":0.3152147239267831,"score_spread":0.2856314458083789,"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."}}