{"id":"W4309342738","doi":"10.1109/smc53654.2022.9945530","title":"COVID-19 Self-Test Guidance System For Swab Collection Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Generalization; Data collection; Telehealth; Software deployment; Test (biology); Sample (material); Sampling (signal processing); Quality (philosophy); Inference; Machine learning; Computer vision; Telemedicine; Statistics; Health care","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":[0.0003576207,0.001219636,0.000656619,0.0007187685,0.0002246491,0.0004573366,0.001730739,0.0007818632,0.003640216],"category_scores_gemma":[0.001158167,0.0004522167,0.0004560957,0.000322893,0.0001676471,0.0007617105,0.001083987,0.0008078345,0.001684482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00099556,"about_ca_system_score_gemma":0.001086395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01125858,"about_ca_topic_score_gemma":0.01640809,"domain_scores_codex":[0.999709,0.00002801116,0.00001850231,0.0001082068,0.00009116612,0.00004516646],"domain_scores_gemma":[0.9997093,0.00006493262,0.00003148521,0.000044159,0.0001080605,0.00004197774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001200652,0.0009527096,0.01402153,0.0006054797,0.0001931875,0.0006811098,0.0001660925,0.04966174,0.03933039,0.001082709,0.1168412,0.7752632],"study_design_scores_gemma":[0.0001220041,0.0003280215,0.005065911,0.00006997194,0.00004019576,0.0002609945,0.00004680338,0.9486455,0.02713283,0.00128484,0.01694865,0.00005431085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1253702,0.002101664,0.6746691,0.0007519531,0.0004681592,0.001057097,0.01100981,0.1751478,0.009424233],"genre_scores_gemma":[0.5681992,0.0009707155,0.3817683,0.001978162,0.00009569354,0.001123669,0.03072747,0.001531768,0.01360509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01125858,"threshold_uncertainty_score":0.02238607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07361408800445832,"score_gpt":0.3460364946927795,"score_spread":0.2724224066883212,"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."}}