{"id":"W3023750470","doi":"10.1101/2020.05.01.20086207","title":"TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centres Intégré Universitaires de Santé et de Services Sociaux; Institut universitaire de cardiologie et de pneumologie de Québec; Jewish General Hospital; McGill University Health Centre; Université Laval; Université de Montréal; McGill University; Centre Hospitalier de l’Université de Montréal; Centre intégré de santé et de services sociaux de Chaudière-Appalaches","funders":"","keywords":"Radiological weapon; Medicine; Receiver operating characteristic; Context (archaeology); Pleural effusion; Radiology; Retrospective cohort study; Coronavirus disease 2019 (COVID-19); Pneumonia; Deep learning; Artificial intelligence; Surgery; Computer science; Internal medicine; Disease","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":[0.006737018,0.001051023,0.0005343176,0.001220453,0.0003385983,0.001044855,0.0009291437,0.001113034,0.0009817614],"category_scores_gemma":[0.01747973,0.0002517279,0.0007345519,0.0004992732,0.0005316389,0.0008429222,0.0012196,0.001002586,0.0005523844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006185807,"about_ca_system_score_gemma":0.0006976966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004550525,"about_ca_topic_score_gemma":0.003184211,"domain_scores_codex":[0.997674,0.0008825521,0.0003056529,0.0005905261,0.000330384,0.0002168301],"domain_scores_gemma":[0.9880741,0.007887162,0.0005758782,0.001291524,0.001786935,0.0003844041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003199002,0.001295306,0.7054094,0.0002330435,0.0006360761,0.0002143256,0.0002186262,0.1002212,0.005671548,0.0003208333,0.002802849,0.1797778],"study_design_scores_gemma":[0.0001470186,0.002100822,0.1142503,0.0001051249,0.0002542071,0.00029591,0.0002542927,0.865857,0.01505168,0.0006753408,0.0009567823,0.00005143042],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830603,0.0005443217,0.01366481,0.0001654799,0.00005797052,0.000140303,0.000953946,0.0003497687,0.001062995],"genre_scores_gemma":[0.9900624,0.0000963174,0.007723613,0.00004074949,0.00002008117,0.00007444892,0.001609997,0.00002047816,0.0003519348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006737018,"threshold_uncertainty_score":0.03562921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2182234563913613,"score_gpt":0.394573361983115,"score_spread":0.1763499055917537,"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."}}