{"id":"W3159395977","doi":"10.1117/1.jmi.8.s1.014502","title":"Deep CNN models for predicting COVID-19 in CT and x-ray images","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); Convolutional neural network; Artificial intelligence; Receiver operating characteristic; Area under curve; Deep learning; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pattern recognition (psychology); Nuclear medicine; Pneumonia; Computed tomography; Radiology; Pathology; Computer science; Disease; Internal medicine","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":[0.0006648166,0.001183186,0.0004132393,0.000879181,0.0001996566,0.0006120294,0.0006867174,0.0008192682,0.001739979],"category_scores_gemma":[0.001950086,0.0002693687,0.0006064894,0.0004611946,0.0002122552,0.0006029285,0.0005724489,0.001054774,0.0007133605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008160371,"about_ca_system_score_gemma":0.0007081628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01558041,"about_ca_topic_score_gemma":0.01559555,"domain_scores_codex":[0.9998239,0.00003140012,0.00001180131,0.00004879806,0.00003517968,0.0000489451],"domain_scores_gemma":[0.9996018,0.0001529114,0.00005862528,0.0000281446,0.0001162756,0.00004221969],"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":[0.001595424,0.0009605017,0.1061239,0.0003362757,0.0004994764,0.0007315419,0.00009265644,0.4555414,0.01701965,0.001522434,0.01639441,0.3991824],"study_design_scores_gemma":[0.000009807386,0.00006909764,0.003293164,0.00002318865,0.00002933165,0.00005573303,0.00001199454,0.9938228,0.001757249,0.0005104716,0.0004107993,0.000006488286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8135322,0.008017375,0.1622484,0.001922086,0.0004845646,0.0002249752,0.004257646,0.003007185,0.006305541],"genre_scores_gemma":[0.9665551,0.001135891,0.02522978,0.0002457244,0.00009472194,0.0000673007,0.003615981,0.00004491486,0.00301054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01558041,"threshold_uncertainty_score":0.03097945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0363969574671866,"score_gpt":0.3727608580027925,"score_spread":0.3363639005356058,"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."}}