{"id":"W3208507240","doi":"10.1109/jbhi.2021.3124967","title":"VidAF: A Motion-Robust Model for Atrial Fibrillation Screening From Facial Videos","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Pattern recognition (psychology); Discriminative model; Convolutional neural network; Computer vision; Facial expression; Speech recognition","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006978142,0.00009296981,0.0004490686,0.0001543401,0.0001532599,0.00003487763,0.00003932536,0.0001181521,0.00001157287],"category_scores_gemma":[0.0002268163,0.00007119616,0.0002277682,0.0001579441,0.00004555253,0.0001595009,0.00001411669,0.0002229621,0.000001294593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005751972,"about_ca_system_score_gemma":0.0005071671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002380938,"about_ca_topic_score_gemma":0.00000399739,"domain_scores_codex":[0.9980909,0.00001933161,0.001186847,0.00005822362,0.0004516517,0.0001931107],"domain_scores_gemma":[0.9984971,0.0001166795,0.0005838441,0.00007769164,0.0002962166,0.0004284936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005110509,0.00007367686,0.008082558,0.001078145,0.0005406484,0.00001967797,0.004917364,0.01327319,0.0002313864,0.00002397282,0.006632099,0.9646162],"study_design_scores_gemma":[0.003318401,0.000404354,0.001241765,0.0005649811,0.0002186127,0.0001109612,0.0007074907,0.983942,0.00008563514,0.0002840139,0.009034931,0.00008687271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1562882,0.0006603088,0.8341611,0.008153974,0.000541342,0.0001036278,0.00006637049,0.00001131168,0.00001377379],"genre_scores_gemma":[0.525346,0.002574383,0.4580966,0.002057673,0.01157693,0.000001296058,0.0001549697,0.00002031105,0.0001717802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9706688,"threshold_uncertainty_score":0.2903295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026697275880817,"score_gpt":0.3605024567697397,"score_spread":0.2578327291816581,"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."}}