{"id":"W3125542913","doi":"10.1038/s41746-020-00369-1","title":"Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT","year":2021,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Sociedade Beneficente Israelita Brasileira Albert Einstein","keywords":"Coronavirus disease 2019 (COVID-19); Pneumonia; Medicine; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Receiver operating characteristic; Convolutional neural network; 2019-20 coronavirus outbreak; Computed tomography; Radiology; Disease; Artificial intelligence; Computer science; Internal medicine; Pathology; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008946385,0.0009310933,0.0005199812,0.001613626,0.0003902382,0.001468292,0.00123576,0.0007155491,0.001617181],"category_scores_gemma":[0.009925504,0.0003365571,0.0005639193,0.001360202,0.0008341928,0.001803941,0.002535274,0.002256505,0.0009572278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009825736,"about_ca_system_score_gemma":0.001460422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003880596,"about_ca_topic_score_gemma":0.004366365,"domain_scores_codex":[0.9982554,0.0006635772,0.000161749,0.0004173415,0.0003507564,0.0001510496],"domain_scores_gemma":[0.9937337,0.002036647,0.0002919556,0.0009869339,0.001664269,0.001286413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005491168,0.0006239151,0.09500975,0.0002385911,0.0002792918,0.0004238757,0.0009282635,0.007758869,0.009443024,0.002306308,0.01421526,0.8682237],"study_design_scores_gemma":[0.0007890312,0.006022883,0.2781568,0.003075465,0.001097158,0.01191185,0.004013433,0.2153763,0.1161685,0.02554553,0.337138,0.0007049704],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6446162,0.03582307,0.2567009,0.01365541,0.0009522535,0.0009974892,0.00446576,0.003212854,0.03957605],"genre_scores_gemma":[0.7596367,0.01644293,0.207263,0.002888929,0.0005682569,0.0003182302,0.005988684,0.0006937184,0.00619966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008946385,"threshold_uncertainty_score":0.04731357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05638066611232208,"score_gpt":0.3712549006133163,"score_spread":0.3148742345009943,"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."}}