{"id":"W4415018104","doi":"10.3389/frai.2025.1653153","title":"Assessment of demographic bias in retinal age prediction machine learning models","year":2025,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"Canada Research Chairs","keywords":"Retinal; Optical coherence tomography; Fundus (uterus); Fundus photography; Predictive modelling; Ethnic group","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008767876,0.0001315653,0.0004139128,0.001085182,0.00004546462,0.00001945713,0.0001174474,0.00008166755,0.00001719306],"category_scores_gemma":[0.0002309997,0.0001276338,0.0001259362,0.00155952,0.0001619441,0.00008590414,0.00003631592,0.0005254102,0.000001019724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095204,"about_ca_system_score_gemma":0.00008488572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004827999,"about_ca_topic_score_gemma":0.0001174647,"domain_scores_codex":[0.9983799,0.0001505093,0.0006799739,0.0003082298,0.0002543877,0.0002269681],"domain_scores_gemma":[0.9995013,0.00005936431,0.0001163625,0.000197311,0.00007850681,0.00004715579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001391928,0.0002434684,0.9091519,0.0001063198,0.00005204679,0.00006008453,0.0002799101,0.03753959,0.001611423,0.002841458,0.0001001122,0.0478745],"study_design_scores_gemma":[0.00008623007,0.000112691,0.04244471,0.0006115708,0.00007996403,0.00000255225,0.001033187,0.9326189,0.003175184,0.01963654,0.0001075358,0.00009094379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3546859,0.0008437115,0.637949,0.0004760904,0.0003389505,0.0002155134,0.00000400782,0.00004091093,0.005445916],"genre_scores_gemma":[0.9820141,0.000483558,0.01698119,0.00005266338,0.00002508121,0.00001385201,0.00002311023,0.000009062077,0.0003974375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8950793,"threshold_uncertainty_score":0.5204757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06101447409068748,"score_gpt":0.3368471451989657,"score_spread":0.2758326711082782,"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."}}