{"id":"W4205812301","doi":"10.1002/alz.054582","title":"A retinal deep phenotyping<sup>TM</sup> platform to predict the cerebral amyloid PET status in older adults","year":2021,"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":"Health Sciences Centre; University of Toronto; Clinique Paro Excellence; Douglas Mental Health University Institute; Hôpital Maisonneuve-Rosemont; Polytechnique Montréal; Concordia University; Centre Hospitalier de l’Université de Montréal; McGill University; Sunnybrook Health Science Centre; Optina Diagnostics (Canada); Greenfield Research (Canada); Montreal Neurological Institute and Hospital","funders":"","keywords":"Retinal; Hyperspectral imaging; Neuroimaging; Artificial intelligence; Dementia; Support vector machine; Pattern recognition (psychology); Positron emission tomography; Retina; Computer science; Classifier (UML); Neuroscience; Pathology; Medicine; Psychology; Disease; Ophthalmology","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":[0.0007940529,0.0005447351,0.0002997966,0.000348169,0.0001086566,0.0003737477,0.0003064078,0.0003382238,0.0009731181],"category_scores_gemma":[0.001158912,0.0001257707,0.0003592591,0.0001490031,0.00009736326,0.0002061133,0.0004171107,0.0002867365,0.0003722276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000283658,"about_ca_system_score_gemma":0.0002501157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003557786,"about_ca_topic_score_gemma":0.003996245,"domain_scores_codex":[0.9998246,0.00005257181,0.000008296043,0.00006656583,0.00002852022,0.00001945936],"domain_scores_gemma":[0.9996761,0.00009362729,0.00006175805,0.00004375715,0.00009070143,0.00003415965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003610301,0.001203051,0.38115,0.0001536428,0.0005756118,0.0005292059,0.0001746309,0.1230733,0.128216,0.0003604413,0.003499881,0.3574539],"study_design_scores_gemma":[0.00004187524,0.00139244,0.1201786,0.00001789878,0.0001330606,0.000398271,0.00005616987,0.8557194,0.02115904,0.0003028009,0.0005734566,0.00002691361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744785,0.0001448559,0.0239256,0.00007970985,0.00001387228,0.00007930359,0.0003404155,0.0004573017,0.000480562],"genre_scores_gemma":[0.9792086,0.00005183632,0.01977771,0.00005589597,0.000008184537,0.00004673376,0.0003628595,0.00001249482,0.0004756997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003557786,"threshold_uncertainty_score":0.007074118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329510394801254,"score_gpt":0.2603070325000253,"score_spread":0.2470119285520128,"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."}}