{"id":"W3211329107","doi":"10.3390/diagnostics11112049","title":"Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study","year":2021,"lang":"en","type":"article","venue":"Diagnostics","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Waterloo; Western University","funders":"","keywords":"Parenchyma; Receiver operating characteristic; Ultrasound; Artificial intelligence; Radiology; Medicine; Computer science; Lung; Deep learning; Machine learning; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003831896,0.0001167847,0.0002494716,0.00005034658,0.0001113268,0.00001812865,0.0001144412,0.00008705806,0.00008878156],"category_scores_gemma":[0.01058846,0.00009698392,0.0001190718,0.000260644,0.00009994403,0.00009986047,0.00003307775,0.0002150576,0.000006463395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000697879,"about_ca_system_score_gemma":0.00006523496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001918577,"about_ca_topic_score_gemma":0.0000547404,"domain_scores_codex":[0.9985707,0.00009460047,0.0006803003,0.0002169742,0.0002840813,0.0001532852],"domain_scores_gemma":[0.9845148,0.01395623,0.0003838256,0.0003436709,0.0007483825,0.00005304038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006692712,0.002127252,0.9687039,0.0003072316,0.0004841013,0.000001830416,0.005312023,0.002044232,0.00782005,0.0005175273,0.000282911,0.01172972],"study_design_scores_gemma":[0.002460064,0.0004069259,0.7650401,0.00007086236,0.0008628872,0.000003070337,0.002647206,0.2261812,0.002071492,0.00003069209,0.0001424743,0.00008297004],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7626891,0.0002528661,0.235172,0.0001912534,0.0002942168,0.001153186,0.00002206162,0.00003145083,0.0001939117],"genre_scores_gemma":[0.9953558,0.0001801313,0.003625018,0.00004175403,0.0001050765,0.0002815343,0.0003573605,0.00001779287,0.00003554043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2326667,"threshold_uncertainty_score":0.9977458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962494229343772,"score_gpt":0.3504355980141519,"score_spread":0.3208106557207142,"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."}}