{"id":"W4302009409","doi":"10.1016/j.compbiomed.2022.106165","title":"Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"The Research Council","keywords":"Coronavirus disease 2019 (COVID-19); Radiomics; Pneumonia; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial intelligence; Machine learning; Computer science; Betacoronavirus; Medicine; Virology; Pathology; Internal medicine; 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":[0.001923462,0.0009110408,0.001192394,0.001451968,0.0004373736,0.001178802,0.001244879,0.001569347,0.001639079],"category_scores_gemma":[0.004094915,0.0004686715,0.001212741,0.0005128728,0.0003203459,0.0007306638,0.001055316,0.001281891,0.0008494905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000388604,"about_ca_system_score_gemma":0.001002798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002798615,"about_ca_topic_score_gemma":0.003206413,"domain_scores_codex":[0.9992023,0.0002061075,0.0001018313,0.0001945019,0.0001650132,0.0001302698],"domain_scores_gemma":[0.9982687,0.0009674591,0.0001264435,0.0001018255,0.0003973692,0.0001381339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005915061,0.003031886,0.3907216,0.0004274428,0.001039698,0.001733951,0.0002811172,0.07910312,0.04175173,0.0008846796,0.004065984,0.4710437],"study_design_scores_gemma":[0.00008987141,0.001007931,0.04305625,0.00003894593,0.0002205051,0.001093045,0.0001136165,0.9385475,0.0142163,0.0007764616,0.000782726,0.00005683096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8545392,0.001158623,0.1395972,0.0004693783,0.0001316318,0.0003788551,0.001035691,0.0009393768,0.001749967],"genre_scores_gemma":[0.9483421,0.0002107201,0.04808836,0.0001329684,0.00006387167,0.0001811124,0.001253339,0.00003204889,0.001695456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002798615,"threshold_uncertainty_score":0.01017231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01867874215538857,"score_gpt":0.3385732267878109,"score_spread":0.3198944846324223,"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."}}