{"id":"W3176049911","doi":"10.2196/25165","title":"Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Receiver operating characteristic; Fundus (uterus); Fundus photography; Optic disc; Medicine; Artificial intelligence; Deep learning; Ophthalmology; Cross-sectional study; Population; Optometry; Computer science; Retinal; Internal medicine; Pathology; Fluorescein angiography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001174732,0.0003217247,0.0003915475,0.0006309613,0.00054778,0.0005425763,0.0003588738,0.0004893449,0.001061236],"category_scores_gemma":[0.003245155,0.0003949297,0.0006352294,0.0006178636,0.0002570185,0.0007212132,0.0004801372,0.0007666243,0.0003387912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477916,"about_ca_system_score_gemma":0.0002648439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006470785,"about_ca_topic_score_gemma":0.006919611,"domain_scores_codex":[0.9994098,0.0001521577,0.00006074642,0.0002154823,0.00009100638,0.00007074396],"domain_scores_gemma":[0.9983634,0.0003081669,0.0004819459,0.0002272735,0.0004677276,0.0001515566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001285069,0.00005373027,0.9974802,0.00001021505,0.00008172786,0.00005828974,0.0001028025,0.00005776496,0.0001352465,0.00002093082,0.0001942761,0.001676242],"study_design_scores_gemma":[0.00001193536,0.0003068676,0.9971337,0.0000176979,0.0001134419,0.0003244211,0.0003735491,0.001062458,0.000121237,0.00007308708,0.0004504723,0.00001092383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984787,0.0002439594,0.0003433817,0.00004473993,0.000007459614,0.00001548167,0.0006059415,0.000004317545,0.000255888],"genre_scores_gemma":[0.9988142,0.0001107443,0.0002546036,0.00004887396,0.000009669705,0.00002205987,0.0005648541,0.000003609289,0.0001714653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006470785,"threshold_uncertainty_score":0.01286626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0302441450440222,"score_gpt":0.3668372410612731,"score_spread":0.336593096017251,"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."}}