{"id":"W2980965120","doi":"10.1038/s41598-019-51503-3","title":"Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":290,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Canada; National Philanthropic Trust; Bill and Melinda Gates Foundation; United States Agency for International Development","keywords":"Triage; Medicine; Tuberculosis; Radiography; Artificial intelligence; Pulmonary tuberculosis; Diagnostic accuracy; Convolutional neural network; Artificial neural network; Machine learning; Radiology; Pathology; Computer science; Medical emergency","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.008736749,0.001222507,0.0008758006,0.001420775,0.0002635047,0.00128894,0.0008916372,0.001331002,0.0005724055],"category_scores_gemma":[0.01813649,0.000320882,0.00118252,0.000863865,0.0005159478,0.001055335,0.001147456,0.000876339,0.0002315424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009346759,"about_ca_system_score_gemma":0.0005419545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00438767,"about_ca_topic_score_gemma":0.002641937,"domain_scores_codex":[0.9960925,0.002146972,0.0003909127,0.0005245472,0.0006495307,0.0001955383],"domain_scores_gemma":[0.9842956,0.01131054,0.00106098,0.0007637056,0.002117051,0.0004521867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0137642,0.002495469,0.4998796,0.000702707,0.003120297,0.0003399767,0.0003996183,0.1239813,0.005182593,0.0002951592,0.001476457,0.3483627],"study_design_scores_gemma":[0.0004550589,0.01095426,0.1874135,0.0001755865,0.00141044,0.0005566631,0.0002672785,0.7873351,0.009173308,0.0009220495,0.001159318,0.0001775495],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98734,0.001112505,0.00953771,0.0002333947,0.0000490985,0.0001122069,0.0004045094,0.0001973713,0.001013193],"genre_scores_gemma":[0.9913132,0.0003018081,0.007314616,0.0001034769,0.00002126688,0.00006106917,0.0006156021,0.00001570742,0.0002533109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008736749,"threshold_uncertainty_score":0.04620486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1088325756948852,"score_gpt":0.3711691323453224,"score_spread":0.2623365566504371,"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."}}