{"id":"W4406753526","doi":"10.1117/12.3042141","title":"Deep reinforcement learning for automatic focus and axial motion correction for OCT B-scans","year":2025,"lang":"en","type":"article","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Reinforcement learning; Computer science; Focus (optics); Motion (physics); Artificial intelligence; Computer vision; Optics; Physics","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":[0.00007623242,0.00007689598,0.00008449428,0.00007501192,0.0001242278,0.00003703908,0.00003774693,0.0000498206,0.00002204133],"category_scores_gemma":[0.00003639187,0.00007932489,0.00004486074,0.000142251,0.00001913994,0.00007474741,0.00000820383,0.00005760184,0.000002443337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000426944,"about_ca_system_score_gemma":0.000005331559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002422594,"about_ca_topic_score_gemma":0.00008453382,"domain_scores_codex":[0.9995598,0.000004132578,0.0001486783,0.0001075492,0.00004107379,0.000138781],"domain_scores_gemma":[0.9996938,0.0001433601,0.00001399932,0.00007877244,0.00003982022,0.00003031462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001621529,0.00002292488,0.0002159526,0.0003187242,0.00008990691,3.548137e-8,0.0001637632,0.5598413,0.001438713,0.1484941,0.001318668,0.2880797],"study_design_scores_gemma":[0.000307843,0.0000601642,0.0004280528,0.00001597788,0.00003724117,3.276675e-7,0.00007682886,0.9922679,0.002018704,0.003233174,0.001473903,0.00007989653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002685991,0.00003398072,0.9811879,0.00008261506,0.0002048739,0.0008442277,5.400814e-7,0.0004041846,0.01455572],"genre_scores_gemma":[0.9894851,0.000005825911,0.009059724,0.00001999711,0.00002618939,0.0008568661,0.00001401302,0.00001038794,0.0005219295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9867991,"threshold_uncertainty_score":0.3234775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007792933182649581,"score_gpt":0.2375295396262444,"score_spread":0.2297366064435948,"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."}}