{"id":"W4396642986","doi":"10.1038/s41598-024-60682-7","title":"Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Biomedical Imaging and Bioengineering; York University","keywords":"Optical coherence tomography; Computer science; SIGNAL (programming language); Artificial intelligence; Signal-to-noise ratio (imaging); Photothermal therapy; Imaging phantom; Coherence (philosophical gambling strategy); Biomedical engineering; Artificial neural network; Contrast (vision); Noise (video); Optics; Computer vision; Physics; Telecommunications; Image (mathematics); Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007796001,0.0005610785,0.0004153712,0.0004119961,0.0001982237,0.0004798707,0.0005484871,0.0008289196,0.000792802],"category_scores_gemma":[0.001698866,0.0001867405,0.0003118906,0.0003846212,0.0003214375,0.0005268762,0.000414003,0.0007422104,0.0001826323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000510727,"about_ca_system_score_gemma":0.0003622098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00237556,"about_ca_topic_score_gemma":0.002183493,"domain_scores_codex":[0.9997761,0.00006722709,0.00001586799,0.00005037036,0.00005938544,0.00003113084],"domain_scores_gemma":[0.9995486,0.000273738,0.00004120107,0.00001445512,0.0001099518,0.0000119539],"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":[0.0002997624,0.0002493583,0.001407687,0.0001400615,0.00008574193,0.0001471552,0.00005155874,0.6996733,0.02753158,0.003581945,0.001597305,0.2652345],"study_design_scores_gemma":[0.000003312489,0.00002736859,0.000139856,0.000003939238,0.000007187572,0.00001122933,0.000002122394,0.997139,0.002158302,0.0003395446,0.0001649128,0.000003351571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1337302,0.00233996,0.8573722,0.0005463398,0.0001766621,0.00008191882,0.0001003758,0.0009162263,0.004736184],"genre_scores_gemma":[0.8296641,0.0007486792,0.1656138,0.0001999516,0.00006511976,0.0001145675,0.0001458759,0.00004563655,0.003402292],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00237556,"threshold_uncertainty_score":0.00472343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113619543985776,"score_gpt":0.2402632978895068,"score_spread":0.2289013434909292,"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."}}