{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002242998,0.0001527316,0.0003338883,0.0001828755,0.00008801929,0.00001238431,0.00005724225,0.00004824053,0.00005026084],"category_scores_gemma":[0.003127446,0.0001134152,0.00007549275,0.0002817424,0.0001818719,0.0001387373,0.00001615474,0.0001423218,0.00002471349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000239274,"about_ca_system_score_gemma":0.00004088919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006957619,"about_ca_topic_score_gemma":0.00000131978,"domain_scores_codex":[0.998934,0.00001806434,0.0002941118,0.0002014057,0.0003573101,0.0001951249],"domain_scores_gemma":[0.9980759,0.001505043,0.000117143,0.0001374699,0.00009263838,0.00007179666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01091848,0.001372947,0.1812567,0.009450643,0.001661697,0.0009339494,0.008232138,0.07652487,0.0524228,0.01037168,0.05640589,0.5904483],"study_design_scores_gemma":[0.02751336,0.0121441,0.6168414,0.008208087,0.0007576099,0.0001310141,0.001337807,0.293941,0.01116163,0.01343874,0.01353803,0.0009872514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5992734,0.003204267,0.0993559,0.03252135,0.001743951,0.005465864,0.001585298,0.001964765,0.2548853],"genre_scores_gemma":[0.9954669,0.00005751841,0.0005845192,0.0002140642,0.000126529,0.00005148019,0.001782034,0.00002216823,0.001694771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.589461,"threshold_uncertainty_score":0.4624936,"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."}}