{"id":"W4400292024","doi":"10.1117/1.jbo.29.s2.s22707","title":"Multispectral label-free in vivo cellular imaging of human retinal pigment epithelium using adaptive optics fluorescence lifetime ophthalmoscopy improves feasibility for low emission analysis and increases sensitivity for detecting changes with age and eccentricity","year":2024,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Eye Institute; National Institutes of Health; University of Rochester; Research to Prevent Blindness","keywords":"Multispectral image; Eccentricity (behavior); Autofluorescence; Retinal pigment epithelium; Phasor; Ophthalmoscopy; Optics; Materials science; Physics; Fluorescence; Computer science; Retina; Artificial intelligence","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":[0.001597305,0.0002318708,0.0007912229,0.000516408,0.00009932161,0.0000684226,0.00008543547,0.0001106453,0.000002149348],"category_scores_gemma":[0.0009207905,0.0001693326,0.0001339675,0.0004922281,0.0005373645,0.0001214307,0.0000918095,0.0003925684,1.008216e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001917181,"about_ca_system_score_gemma":0.0001381883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001004775,"about_ca_topic_score_gemma":0.00001131447,"domain_scores_codex":[0.9981989,0.0000893509,0.0005752709,0.0003423549,0.0004351583,0.0003589785],"domain_scores_gemma":[0.998252,0.000636699,0.0003020691,0.0001813759,0.0002956335,0.0003322107],"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":[0.000939747,0.0005426175,0.01503409,0.001013644,0.0002887014,0.0006573024,0.0001995793,0.000005662654,0.9797974,0.00004638033,0.00001083342,0.00146409],"study_design_scores_gemma":[0.002035396,0.003610982,0.003221064,0.002210858,0.001644141,0.000359781,0.000314921,0.5093666,0.4767183,0.0003166453,0.000001547764,0.0001998356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8284642,0.0004772928,0.1699797,0.0005067478,0.00003923473,0.0004484388,0.00005774298,0.00002232875,0.000004343042],"genre_scores_gemma":[0.7167547,0.00008334054,0.2829842,0.00001711271,0.0001312171,0.000003031633,0.000003435026,0.00001830927,0.000004631583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5093609,"threshold_uncertainty_score":0.6905183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061547049233497,"score_gpt":0.3274119498073773,"score_spread":0.3067964793150423,"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."}}