{"id":"W4293432393","doi":"10.2196/36660","title":"Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation","year":2022,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health; Louisiana State University; National Science Foundation","keywords":"Convolutional neural network; Computer science; Coronavirus disease 2019 (COVID-19); Workload; Artificial intelligence; Radiography; Pattern recognition (psychology); Region of interest; Medicine; Radiology; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001423169,0.001724838,0.0007260363,0.0006995265,0.0003212628,0.0005631743,0.001791301,0.001210958,0.001491292],"category_scores_gemma":[0.002437872,0.0004615403,0.0008460017,0.000506249,0.0002945834,0.0008204049,0.0008107561,0.001321786,0.0004453513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001832176,"about_ca_system_score_gemma":0.001754903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02772223,"about_ca_topic_score_gemma":0.02376331,"domain_scores_codex":[0.9996738,0.00005503638,0.00002910465,0.00008971089,0.00007871097,0.00007365819],"domain_scores_gemma":[0.9992755,0.0002767288,0.00006885284,0.00005252099,0.0002898404,0.00003653704],"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":[0.0005552644,0.0004248879,0.01064232,0.0002705712,0.0002686248,0.0002081676,0.00005019949,0.7885885,0.00704723,0.0008463598,0.003893151,0.1872048],"study_design_scores_gemma":[0.000005850356,0.00005251634,0.000491811,0.000007036453,0.00001853641,0.00001263512,0.000004395039,0.9979435,0.001203845,0.0001214928,0.000134772,0.0000037076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6789756,0.008505157,0.2947015,0.001638577,0.0005427835,0.0007428089,0.00175859,0.004927201,0.00820775],"genre_scores_gemma":[0.9038424,0.001273348,0.08628772,0.0003398123,0.00007237326,0.00051038,0.002293756,0.00007704035,0.005303166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02772223,"threshold_uncertainty_score":0.05512178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08418977341496674,"score_gpt":0.3359622127543891,"score_spread":0.2517724393394223,"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."}}