{"id":"W4393373943","doi":"10.1002/pd.6559","title":"Deep‐learning computer vision can identify increased nuchal translucency in the first trimester of pregnancy","year":2024,"lang":"en","type":"article","venue":"Prenatal Diagnosis","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Imperial Bank of Commerce (Canada); Vector Institute; Canadian Institute for Advanced Research; Mount Sinai Hospital; Centre for Social Innovation; Hospital for Sick Children; University of Toronto","funders":"Mount Sinai Health System","keywords":"Fetus; Segmentation; Medicine; 3D ultrasound; Pregnancy; Ultrasound; Fetal head; Convolutional neural network; Prenatal diagnosis; Gestation; Obstetrics; Nuclear medicine; Artificial intelligence; Radiology; Computer science; 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":[0.0002909387,0.0002142295,0.0003231341,0.0002190743,0.00007216444,0.00008208387,0.0002472591,0.0001347257,0.00009310099],"category_scores_gemma":[0.0002844837,0.0001508218,0.000198851,0.0004830259,0.00008625091,0.0001789315,0.00008552705,0.0004797147,0.00003156904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004467554,"about_ca_system_score_gemma":0.00005462883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009487598,"about_ca_topic_score_gemma":0.0002878108,"domain_scores_codex":[0.9983439,0.0001279728,0.0004164521,0.0003776875,0.0004222835,0.0003116896],"domain_scores_gemma":[0.9981063,0.001384592,0.00005633874,0.0002982208,0.0000466518,0.000107879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003719976,0.001639492,0.6207834,0.004212121,0.0002573008,0.002769073,0.0110822,0.00049684,0.0001212655,0.0009093133,0.004024758,0.3533322],"study_design_scores_gemma":[0.002793406,0.001537428,0.9471553,0.01880298,0.0004025467,0.0002002935,0.0002227155,0.01907942,0.005210581,0.0002715961,0.003899588,0.0004241501],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749064,0.01694315,0.001707395,0.00300974,0.0004949309,0.001030849,0.00007331072,0.0002142177,0.001619951],"genre_scores_gemma":[0.9980062,0.0008454741,0.0004308408,0.0002322612,0.0001377567,0.0001628566,0.000119783,0.00003191977,0.00003291093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.352908,"threshold_uncertainty_score":0.6150333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446465153376643,"score_gpt":0.2826172142797528,"score_spread":0.2681525627459864,"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."}}