{"id":"W4392376857","doi":"10.3390/s24051664","title":"COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; National Research Council Canada; University of Waterloo","funders":"National Research Council Canada","keywords":"Convolutional neural network; Workflow; Artificial intelligence; Deep learning; Ultrasound; Computer science; Coronavirus disease 2019 (COVID-19); Artificial neural network; Pandemic; Machine learning; Medicine; Radiology; Pathology; Database","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":[0.001333128,0.00126548,0.0008952526,0.000582098,0.0003864978,0.001045874,0.002290425,0.001980806,0.00252471],"category_scores_gemma":[0.004157916,0.0006044478,0.0009410887,0.0004128815,0.001014253,0.001169286,0.002002962,0.00275177,0.0006417631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001240415,"about_ca_system_score_gemma":0.001899705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01146344,"about_ca_topic_score_gemma":0.01303791,"domain_scores_codex":[0.9994308,0.0001767501,0.00002503182,0.0001467549,0.0001378484,0.00008278967],"domain_scores_gemma":[0.9988167,0.000592093,0.0001269924,0.0001010923,0.0002867431,0.00007633939],"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":[0.000163752,0.00009630353,0.001726566,0.00009668792,0.00007476516,0.00016181,0.0000667257,0.8885633,0.003460983,0.007734292,0.003774675,0.09408011],"study_design_scores_gemma":[0.000002556516,0.00001469736,0.00005831409,0.00000377637,0.000003508428,0.000007988494,0.000001804672,0.9981958,0.0003116877,0.001188311,0.0002083893,0.000003177315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01634478,0.0004952,0.9790736,0.0006930158,0.00006011687,0.00006140526,0.0002364785,0.001517697,0.001517718],"genre_scores_gemma":[0.6971797,0.0006127991,0.2900556,0.001195683,0.0001720283,0.0004084877,0.001464221,0.0004113075,0.008500196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01146344,"threshold_uncertainty_score":0.02279341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03672456628615496,"score_gpt":0.4032123233713369,"score_spread":0.3664877570851819,"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."}}