{"id":"W4379382289","doi":"10.1109/jbhi.2023.3282596","title":"Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Drug Abuse; National Institutes of Health; Massachusetts Life Sciences Center","keywords":"Artificial intelligence; Leverage (statistics); Computer science; Receiver operating characteristic; Deep learning; Feature engineering; Machine learning; Pattern recognition (psychology)","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":[0.002054906,0.001867394,0.001050028,0.001543855,0.0003785396,0.0009495413,0.001585782,0.001619798,0.00108227],"category_scores_gemma":[0.005229945,0.0004540574,0.001009196,0.001010379,0.0004630677,0.001157013,0.001329611,0.001952836,0.0009211373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214338,"about_ca_system_score_gemma":0.001250959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01587086,"about_ca_topic_score_gemma":0.01376374,"domain_scores_codex":[0.9986828,0.00039364,0.00008999521,0.0003717672,0.0002534325,0.0002083757],"domain_scores_gemma":[0.9981268,0.001032078,0.0001884725,0.0001815815,0.0003623914,0.0001085965],"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":[0.000987791,0.0005649091,0.01259823,0.0004087692,0.000310549,0.0003107745,0.000157942,0.2655135,0.01227326,0.001169151,0.02110196,0.6846032],"study_design_scores_gemma":[0.00002299141,0.00007817113,0.001489655,0.00002757312,0.00002270328,0.00004739569,0.00002817808,0.9926379,0.004043062,0.0007597742,0.0008310211,0.00001164717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4028864,0.0107757,0.5592105,0.002216501,0.0004790847,0.0002959549,0.004796559,0.01465724,0.004682138],"genre_scores_gemma":[0.8276713,0.001720329,0.1562739,0.0006732395,0.0001728443,0.0001957849,0.009070138,0.0002689933,0.003953591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01587086,"threshold_uncertainty_score":0.03155696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04892436530114649,"score_gpt":0.3886009924711608,"score_spread":0.3396766271700142,"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."}}