{"id":"W2945053806","doi":"10.21037/qims.2019.05.17","title":"Multi-scale and -contrast sensorless adaptive optics optical coherence tomography","year":2019,"lang":"en","type":"article","venue":"Quantitative Imaging in Medicine and Surgery","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Retinal; Optical coherence tomography; Adaptive optics; Retina; Retinal pigment epithelium; Contrast (vision); Optics; Computer science; Preclinical imaging; Visualization; Image quality; Biomedical engineering; Computer vision; Artificial intelligence; Ophthalmology; In vivo; Medicine; Physics; Biology","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":[0.0004036791,0.0003286573,0.0001371949,0.000460486,0.000119593,0.0004294884,0.0004476238,0.0004296264,0.0009172728],"category_scores_gemma":[0.0007344728,0.0001555533,0.0001468961,0.0003049884,0.0003947895,0.000527486,0.000361277,0.0002635783,0.0002073225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003361786,"about_ca_system_score_gemma":0.0003189219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005193182,"about_ca_topic_score_gemma":0.001381936,"domain_scores_codex":[0.9996784,0.00005336907,0.0000162567,0.00006123955,0.0001732149,0.00001764049],"domain_scores_gemma":[0.9995679,0.0001489997,0.0001174555,0.00005522744,0.00008628843,0.00002412103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001518763,0.00005933901,0.003655526,0.0001219818,0.00002289669,0.00008866988,0.00003579403,0.00339693,0.9461915,0.001023004,0.0004374018,0.04481518],"study_design_scores_gemma":[0.00006763171,0.00041863,0.0187919,0.00003040228,0.000058244,0.002037172,0.0000444028,0.1643099,0.805414,0.001203499,0.00754847,0.0000756524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4563072,0.001545962,0.5360855,0.0004779581,0.00005672102,0.0001579926,0.0003821867,0.0006855542,0.004300961],"genre_scores_gemma":[0.6654145,0.0005972611,0.3320556,0.0002034262,0.0000382961,0.00009729242,0.000205676,0.00004518842,0.001342731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009172728,"threshold_uncertainty_score":0.003068626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03340744002882708,"score_gpt":0.2837487741202061,"score_spread":0.250341334091379,"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."}}