{"id":"W4413257735","doi":"10.1016/j.compbiomed.2025.110844","title":"Learning-based autonomous navigation, benchmark environments and simulation framework for endovascular interventions","year":2025,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Centre For Medical Engineering, King’s College London; Engineering and Physical Sciences Research Council; King's College London; National Institute for Health and Care Research","keywords":"Benchmark (surveying); Computer science; Psychological intervention; Artificial intelligence; Simulation; Human–computer interaction; Medicine; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006788855,0.00007583522,0.0001867965,0.0001423317,0.0004684251,0.000002685354,0.00003678466,0.0002055269,0.00002140401],"category_scores_gemma":[0.0004954463,0.0000651658,0.000021381,0.0001099125,0.000113039,0.00002723667,0.00002685638,0.0002859605,7.486794e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005276907,"about_ca_system_score_gemma":0.00005519424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003563963,"about_ca_topic_score_gemma":0.00001336996,"domain_scores_codex":[0.9989579,0.0003097139,0.0003588395,0.0002017887,0.00002598719,0.000145733],"domain_scores_gemma":[0.9984924,0.001257222,0.00007936479,0.00008942719,0.00003902968,0.00004250768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001599599,0.0001480602,0.5472033,0.001484383,0.0001070842,0.000002162713,0.00515762,0.2289764,0.0001052704,0.1272739,0.0003646716,0.08901709],"study_design_scores_gemma":[0.002850792,0.0004726611,0.07599045,0.002948934,0.000044352,4.487567e-7,0.0005930162,0.8812805,0.000004036121,0.02138064,0.01431305,0.0001211118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09850676,0.0008766368,0.8950802,0.004276695,0.0005720305,0.0006153042,0.000002621753,0.00001882552,0.00005093464],"genre_scores_gemma":[0.9576458,0.000169617,0.04024332,0.001353619,0.00009206519,0.0001102678,0.0002779724,0.00000471869,0.0001026317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.859139,"threshold_uncertainty_score":0.3602794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04569780869512996,"score_gpt":0.4639466148303791,"score_spread":0.4182488061352492,"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."}}