{"id":"W4210550903","doi":"10.24908/pocus.v7ikidney.15345","title":"Machine Learning in Point of Care Ultrasound","year":2022,"lang":"en","type":"review","venue":"POCUS Journal","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; University of Pennsylvania; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Howard Hughes Medical Institute","keywords":"Point of care ultrasound; Computer science; Medical physics; Patient care; Point (geometry); Medical imaging; Point of care; Point-of-care testing; Software; Artificial intelligence; Machine learning; Medicine; Ultrasound; Radiology; Pathology; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00220141,0.000931663,0.00176853,0.002462848,0.0002452563,0.001462642,0.001203869,0.001899145,0.003488742],"category_scores_gemma":[0.005103647,0.0004105332,0.001185432,0.002569764,0.000708936,0.00163421,0.0008453449,0.002859427,0.001569595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102048,"about_ca_system_score_gemma":0.001487376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002079232,"about_ca_topic_score_gemma":0.001892603,"domain_scores_codex":[0.9991472,0.0003237545,0.00009840328,0.0001374076,0.0002448661,0.00004840645],"domain_scores_gemma":[0.9970253,0.002317588,0.0001897373,0.00005170388,0.000357248,0.00005842178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005909089,0.00007927758,0.0004766443,0.01668845,0.0002913036,0.0001071386,0.00005984756,0.002343235,0.0001817454,0.007863011,0.02144773,0.9504025],"study_design_scores_gemma":[0.00007017538,0.0003870922,0.004559176,0.03399742,0.0005850718,0.001995116,0.0001722618,0.005709132,0.0009029334,0.0336867,0.9177875,0.0001474527],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001447297,0.997347,0.000935903,0.0005542728,0.0002252833,0.000009138401,0.00002061292,0.00001147781,0.0007516424],"genre_scores_gemma":[0.003608386,0.993897,0.001129834,0.0003733194,0.000485849,0.00002207734,0.00004965346,0.000005389794,0.0004285218],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003488742,"threshold_uncertainty_score":0.01167101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07692418482058493,"score_gpt":0.405272962719853,"score_spread":0.328348777899268,"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."}}