{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002616395,0.0001429481,0.0005075547,0.0002770262,0.00008005159,0.0000658288,0.000150149,0.00005020505,0.0001268625],"category_scores_gemma":[0.01614432,0.0001220028,0.0001441247,0.0002344019,0.0001375693,0.000284648,0.0001106169,0.0006591693,8.059309e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003179022,"about_ca_system_score_gemma":0.001708766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007748386,"about_ca_topic_score_gemma":0.00002149544,"domain_scores_codex":[0.9975773,0.0001101546,0.0007283594,0.0002431279,0.001016632,0.0003244441],"domain_scores_gemma":[0.996585,0.001914897,0.0002541105,0.0001575379,0.0002571332,0.0008313939],"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":[0.000796369,0.001961184,0.3687952,0.004369867,0.0005491304,0.06320203,0.006545452,0.01302745,0.007140208,0.0004522821,0.1204509,0.4127099],"study_design_scores_gemma":[0.01218661,0.0001087889,0.006016937,0.003080514,0.0003541578,0.009404909,0.002270838,0.9190099,0.0007739341,0.003010361,0.04352294,0.000260109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.100107,0.01233047,0.2972927,0.5891948,0.0006330048,0.0002807252,0.000004654784,0.00004633148,0.00011026],"genre_scores_gemma":[0.9078108,0.001498616,0.01152966,0.07813268,0.0009157784,0.00001062969,0.000004727482,0.00004277362,0.00005435931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9059824,"threshold_uncertainty_score":0.9921431,"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."}}