{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027958,0.0008964365,0.00105018,0.0008327319,0.000541614,0.001208105,0.001042811,0.001120913,0.001249431],"category_scores_gemma":[0.06128152,0.0004194443,0.001162438,0.0005644869,0.0007234201,0.0009183065,0.001314589,0.001582837,0.0004543625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008131008,"about_ca_system_score_gemma":0.001122253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007223723,"about_ca_topic_score_gemma":0.00584067,"domain_scores_codex":[0.9952295,0.00290337,0.0004843498,0.0007128858,0.0004491622,0.0002208583],"domain_scores_gemma":[0.9492475,0.04078978,0.003178346,0.00314328,0.003122475,0.0005186994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001213429,0.0001868275,0.7303444,0.0001283339,0.001257361,0.0002743448,0.0003603124,0.2251934,0.001081417,0.001262832,0.001955458,0.03674206],"study_design_scores_gemma":[0.00007219102,0.0003852698,0.08583175,0.0001222745,0.0002762861,0.0002962565,0.0002161471,0.9047223,0.001807868,0.004691496,0.001520081,0.00005803185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9424596,0.001378873,0.05186125,0.0009824788,0.0001168714,0.0001233958,0.001358208,0.0003231982,0.001396041],"genre_scores_gemma":[0.9902068,0.0001229531,0.008027002,0.0001755638,0.00003215785,0.00006711539,0.0009802821,0.00002729714,0.0003608537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.027958,"threshold_uncertainty_score":0.1478578,"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."}}