{"id":"W3178969508","doi":"10.1016/j.ultrasmedbio.2021.05.011","title":"Automatically Delineating Key Anatomy in 3-D Ultrasound Volumes for Hip Dysplasia Screening","year":2021,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Hip disorders and treatments","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Nvidia","keywords":"Convolutional neural network; Segmentation; Computer science; Pelvis; Dice; Artificial intelligence; Sørensen–Dice coefficient; Random forest; Test set; Femoral head; Ultrasound; 3D ultrasound; Pattern recognition (psychology); Anatomy; Medicine; Radiology; Image segmentation; Mathematics","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":[0.000919821,0.001189225,0.0009701988,0.004594408,0.0004351101,0.001995792,0.0012065,0.001594667,0.002222385],"category_scores_gemma":[0.004250336,0.001020437,0.001208969,0.001450248,0.0003605516,0.0007667661,0.001591943,0.0009292291,0.001929558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004175084,"about_ca_system_score_gemma":0.001675658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007059455,"about_ca_topic_score_gemma":0.01149853,"domain_scores_codex":[0.9994314,0.00009332129,0.0000604538,0.0001246891,0.00020363,0.00008650759],"domain_scores_gemma":[0.9985561,0.0007151901,0.0001524415,0.0001287244,0.0003540863,0.00009341765],"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":[0.0007591208,0.000198644,0.01697168,0.0007481226,0.0001799921,0.0009382531,0.0004247745,0.02424385,0.1176567,0.001665842,0.01354597,0.8226671],"study_design_scores_gemma":[0.0001392472,0.0003719366,0.03320323,0.0003576952,0.0004260934,0.005102906,0.0005776373,0.8117404,0.118004,0.006006698,0.02389512,0.000175001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1104768,0.003890634,0.8639009,0.00079996,0.0002026404,0.0004911144,0.002697744,0.01480699,0.002733135],"genre_scores_gemma":[0.3297215,0.00200871,0.6606156,0.0004606312,0.0001552151,0.0002434474,0.003151282,0.001492623,0.002150999],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007059455,"threshold_uncertainty_score":0.01403672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02310505436641575,"score_gpt":0.3387603429542976,"score_spread":0.3156552885878818,"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."}}