{"id":"W4390194888","doi":"10.1002/alz.079569","title":"Hyperspectral retinal imaging as a biomarker for Alzheimer’s disease","year":2023,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optina Diagnostics (Canada)","funders":"","keywords":"Hyperspectral imaging; Retinal; Dementia; Retina; Biomarker; Medicine; Amyloid (mycology); Pathology; Artificial intelligence; Pattern recognition (psychology); Ophthalmology; Computer science; Neuroscience; Disease; Psychology; 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":[0.0006580145,0.0003210797,0.0002465253,0.001053173,0.0001703067,0.0006153208,0.0002074184,0.0003423628,0.001565311],"category_scores_gemma":[0.0008707516,0.0001534642,0.0001864599,0.0006095869,0.0002070501,0.0003119013,0.0002710698,0.0002666357,0.000227544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001922587,"about_ca_system_score_gemma":0.00008893078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009121708,"about_ca_topic_score_gemma":0.001370113,"domain_scores_codex":[0.999726,0.00008646635,0.00002157951,0.00007147635,0.00006996684,0.00002447547],"domain_scores_gemma":[0.9994276,0.0001022467,0.0002524026,0.00004693621,0.0001245013,0.00004633669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003178558,0.0003370938,0.6869319,0.0004282244,0.00058655,0.0003848661,0.0003027771,0.001033884,0.1910047,0.0005836921,0.002051687,0.113176],"study_design_scores_gemma":[0.0000261046,0.0002714694,0.9778345,0.00003807925,0.0001135526,0.001108828,0.0001344013,0.003803386,0.01509278,0.0004460808,0.001112937,0.00001790253],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913232,0.002884612,0.002860988,0.0001318807,0.0000196995,0.00002835603,0.0005258195,0.00005059026,0.002174806],"genre_scores_gemma":[0.993625,0.0006919326,0.004779947,0.000069163,0.00003319338,0.00002322125,0.0002782757,0.000006347089,0.0004928931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001565311,"threshold_uncertainty_score":0.005236447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04241413694170688,"score_gpt":0.3341995960906968,"score_spread":0.2917854591489899,"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."}}