{"id":"W4308737348","doi":"10.1038/s41467-022-34245-1","title":"A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian VIGOUR Centre; University of Alberta","funders":"","keywords":"Workflow; Confidence interval; Deep learning; Metric (unit); Artificial intelligence; Computer science; Machine learning; Interpretation (philosophy); Ejection fraction; Equivalence (formal languages); Statistics; Medicine; Internal medicine; Heart failure; Mathematics","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":[0.0006058789,0.00006225776,0.000152205,0.00008597207,0.000469284,0.000006502101,0.0003762968,0.00007625105,0.00001795448],"category_scores_gemma":[0.0004269311,0.00003832831,0.0004901448,0.0005768028,0.00009782062,0.00003345904,0.0001565479,0.0006579405,3.777872e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006111656,"about_ca_system_score_gemma":0.00007642962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001678469,"about_ca_topic_score_gemma":0.0000246453,"domain_scores_codex":[0.9990897,0.0002829644,0.0002095276,0.00007380173,0.0002670581,0.00007698655],"domain_scores_gemma":[0.9979891,0.0004909394,0.00018029,0.0010758,0.0002471984,0.00001670136],"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":[0.00120528,0.0007838544,0.1029284,0.000174841,0.002136241,2.756473e-7,0.00413433,0.7714157,0.001879759,0.001866624,0.004889632,0.1085851],"study_design_scores_gemma":[0.00118257,0.0001531928,0.08015826,0.00003376511,0.0006393886,0.000009985242,0.0009273583,0.8582952,0.001611363,0.00002792778,0.05689655,0.00006444542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3596137,0.03998104,0.529055,0.04444436,0.00486264,0.01569337,0.0004047452,0.001728609,0.004216493],"genre_scores_gemma":[0.9981362,0.00006454866,0.001127247,0.0002013791,0.00001377181,0.0002146837,0.0001986716,0.00001151438,0.00003199412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6385224,"threshold_uncertainty_score":0.36094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209022669399039,"score_gpt":0.2889978502896969,"score_spread":0.2769076235957065,"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."}}