{"id":"W3197741533","doi":"10.1016/j.cjca.2021.08.014","title":"A Deep Learning–Enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada Syndrome","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Taipei Veterans General Hospital; National Health Research Institutes; Ministry of Science and Technology, Taiwan; Academia Sinica","keywords":"Medicine; Brugada syndrome; Deep learning; Internal medicine; Right bundle branch block; Cardiology; Medical diagnosis; Sudden cardiac death; Receiver operating characteristic; Cardiac arrhythmia; Electrocardiography; Artificial intelligence; Machine learning; Atrial fibrillation; Radiology; Computer science","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":[0.0002450209,0.0004836305,0.0004321766,0.0003214114,0.0001817193,0.0003742861,0.0006342827,0.0008027247,0.0009518485],"category_scores_gemma":[0.001089095,0.0001901341,0.0003832954,0.0001923223,0.0001221154,0.000260416,0.0004077989,0.001026057,0.0003218671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000346099,"about_ca_system_score_gemma":0.0005941619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01072427,"about_ca_topic_score_gemma":0.01181692,"domain_scores_codex":[0.9999226,0.0000129748,0.000004864431,0.0000273699,0.00001329559,0.00001874775],"domain_scores_gemma":[0.9997565,0.0001287695,0.00001849263,0.00001442932,0.00006021691,0.00002166823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005644885,0.0003547537,0.01608723,0.00008504104,0.0001885285,0.001160083,0.00006006287,0.706475,0.01004429,0.002342578,0.008029694,0.2546082],"study_design_scores_gemma":[0.000005231036,0.00001210928,0.0005485701,0.000003676053,0.000008317662,0.00005053375,0.000002017587,0.9983429,0.0003759901,0.0005009327,0.0001469969,0.000002806945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3446373,0.002091343,0.642789,0.002005691,0.0003263573,0.00006398062,0.001803472,0.002916449,0.003366443],"genre_scores_gemma":[0.9674463,0.0002921616,0.02841485,0.000214529,0.000065034,0.00003850908,0.0009363687,0.00003704766,0.002555049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01072427,"threshold_uncertainty_score":0.02132368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075242123868973,"score_gpt":0.2351053922901514,"score_spread":0.2243529710514617,"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."}}