{"id":"W3114166611","doi":"10.3389/fmed.2020.608525","title":"COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images","year":2020,"lang":"en","type":"article","venue":"Frontiers in Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":347,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Convolutional neural network; Leverage (statistics); Coronavirus disease 2019 (COVID-19); Medicine; Radiology; Artificial intelligence; Disease; Computer science; Pathology; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007289029,0.001166794,0.0003279999,0.0004347592,0.0001788055,0.0004128497,0.001321363,0.0008599697,0.001746758],"category_scores_gemma":[0.00189896,0.0003691702,0.0005107698,0.0002100913,0.0003667397,0.0005744151,0.0006437562,0.001025932,0.0003366648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000864593,"about_ca_system_score_gemma":0.001180778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005443267,"about_ca_topic_score_gemma":0.01191152,"domain_scores_codex":[0.9997762,0.00004846354,0.0000136949,0.00006763932,0.00005818093,0.0000358197],"domain_scores_gemma":[0.9995851,0.0001804886,0.00005651884,0.00003250696,0.0001186989,0.00002672359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003173969,0.0001980735,0.006597283,0.000295243,0.0001085032,0.0002886224,0.00005462623,0.8508596,0.01825451,0.002884609,0.004972058,0.1151695],"study_design_scores_gemma":[0.00001261378,0.00007731032,0.0003405983,0.000007949675,0.000009740535,0.00002980396,0.000004155292,0.9958571,0.002468493,0.0006221364,0.0005650927,0.000004990851],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2395843,0.001366183,0.7413735,0.0008453086,0.0001868867,0.0005603745,0.001649931,0.006603383,0.00783009],"genre_scores_gemma":[0.7489036,0.000342964,0.2422157,0.0004907334,0.00002907551,0.0004789228,0.00271104,0.0002274728,0.004600525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005443267,"threshold_uncertainty_score":0.01082319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05675796477487922,"score_gpt":0.3146544662007718,"score_spread":0.2578965014258925,"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."}}