{"id":"W4285676883","doi":"10.1002/mp.15856","title":"Deep learning in ultrasound elastography imaging: A review","year":2022,"lang":"en","type":"review","venue":"Medical Physics","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds de recherche du Québec","keywords":"Elastography; Ultrasound elastography; Deep learning; Ultrasound; Convolutional neural network; Artificial intelligence; Computer science; Radiology; Magnetic resonance elastography; Perceptron; Artificial neural network; Biomedical engineering; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001115554,0.0007180189,0.003151444,0.0004134474,0.0001675462,0.00003221143,0.0004837016,0.0002105623,0.001687476],"category_scores_gemma":[0.001796716,0.0005904975,0.001754392,0.002894794,0.0003449896,0.0000877861,0.0001543892,0.004552499,0.0001509237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001846508,"about_ca_system_score_gemma":0.0006706658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003423494,"about_ca_topic_score_gemma":0.000002819766,"domain_scores_codex":[0.9952042,0.0004454719,0.001092818,0.00085095,0.001649123,0.0007574845],"domain_scores_gemma":[0.9971123,0.001247198,0.0003934866,0.0006597665,0.00004820957,0.0005390682],"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":[0.00000450478,0.0003302918,0.0006592353,0.04638129,0.0001742439,0.0001121496,0.00005354383,2.456345e-7,8.814238e-8,0.00002535765,0.001848441,0.9504106],"study_design_scores_gemma":[0.0003936323,0.00008535383,0.00001825028,0.07109902,0.001556115,0.0008442702,0.00002114384,0.000008780906,3.342208e-8,0.00008142654,0.9254292,0.000462783],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001112808,0.992807,0.001225841,0.0002194272,0.0004180152,0.00085523,0.000007018001,0.0002372066,0.004229169],"genre_scores_gemma":[0.00000865935,0.9955838,0.0001572198,0.002164349,0.0005942415,0.0003593014,0.0008063344,0.0001589262,0.000167162],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9499478,"threshold_uncertainty_score":0.9996547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271695077744697,"score_gpt":0.3166168110128792,"score_spread":0.2938998602354322,"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."}}