{"id":"W4323807075","doi":"10.1038/s41746-023-00774-2","title":"Machine learning for accurate estimation of fetal gestational age based on ultrasound images","year":2023,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"Engineering and Physical Sciences Research Council; University of Oxford; National Institute for Health and Care Research; Bill and Melinda Gates Foundation","keywords":"Gestational age; Crown-rump length; Ultrasound; Medicine; Obstetrics; Pregnancy; Gestation; 3D ultrasound; Fetus; First trimester; Radiology; Biology","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":[0.004386839,0.001117959,0.001152312,0.00154765,0.0003367617,0.001257261,0.001478728,0.001587372,0.00113691],"category_scores_gemma":[0.018868,0.0004431422,0.001033706,0.0008831709,0.0005821247,0.001086609,0.0009630219,0.002206553,0.001266679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001039789,"about_ca_system_score_gemma":0.001065343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007072416,"about_ca_topic_score_gemma":0.003850543,"domain_scores_codex":[0.9979836,0.0007409543,0.0001696921,0.0006000231,0.0003807259,0.0001250203],"domain_scores_gemma":[0.9936689,0.004153321,0.0005269765,0.0005376026,0.001023065,0.00009014375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003455557,0.0001785043,0.01980528,0.0002811576,0.0002440808,0.0002047772,0.000118532,0.514896,0.009226123,0.003689,0.007180891,0.44383],"study_design_scores_gemma":[0.00000949441,0.00003135183,0.00221807,0.00003547966,0.00001418199,0.00005235074,0.000008990463,0.9919025,0.002211075,0.002697595,0.0008019696,0.00001686326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04885695,0.002937585,0.9411185,0.0008828734,0.0002026627,0.0001228791,0.001015444,0.003356563,0.001506593],"genre_scores_gemma":[0.6759404,0.001461472,0.3157643,0.0005563644,0.0002555276,0.0003959004,0.003367904,0.0002884874,0.001969648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007072416,"threshold_uncertainty_score":0.02320009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03478213172163702,"score_gpt":0.3143024793343034,"score_spread":0.2795203476126664,"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."}}