{"id":"W4411682666","doi":"10.1055/a-2643-9818","title":"Automated breast ultrasound features associated with diagnostic performance of a multiview convolutional neural network according to the level of experience of radiologists","year":2025,"lang":"en","type":"article","venue":"Ultraschall in der Medizin - European Journal of Ultrasound","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Medicine; Homogeneous; Ultrasound; Receiver operating characteristic; Breast ultrasound; Radiology; Nuclear medicine; Area under curve; Lesion; Diagnostic accuracy; Area under the curve; Convolutional neural network; Internal medicine; Breast cancer; Pathology; Mammography; Artificial intelligence; Mathematics; Cancer; Computer science","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":[0.001346175,0.0003572121,0.0002663628,0.0008339254,0.0001101943,0.000477004,0.0002423253,0.0003566334,0.0008298367],"category_scores_gemma":[0.006990096,0.0001550243,0.0003487826,0.0003089837,0.0001818424,0.0004967708,0.0005010503,0.0001830518,0.0001805654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003232968,"about_ca_system_score_gemma":0.0001940838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001962618,"about_ca_topic_score_gemma":0.002228778,"domain_scores_codex":[0.9992875,0.0001623432,0.00005989012,0.0002011571,0.0001655961,0.0001234653],"domain_scores_gemma":[0.9962158,0.001806698,0.0007707314,0.0001884376,0.0007823488,0.0002359787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001355181,0.0001797737,0.8720453,0.00007656759,0.0003476436,0.0002556333,0.0001482375,0.01453376,0.02156703,0.00008090922,0.0005901923,0.08881976],"study_design_scores_gemma":[0.00002916072,0.0009204024,0.8069539,0.00003381813,0.0002588403,0.0007175721,0.0001902706,0.1722188,0.01776996,0.0003103277,0.000553871,0.00004300503],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996304,0.0001867007,0.002956612,0.00003740189,0.000009439271,0.000007617898,0.0001025474,0.00004471736,0.0003509321],"genre_scores_gemma":[0.9983788,0.00003390885,0.001239831,0.00001429576,0.00000884805,0.000004701418,0.0001688371,0.000005841665,0.0001449184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001962618,"threshold_uncertainty_score":0.007119298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03069882490577965,"score_gpt":0.2729425405721527,"score_spread":0.2422437156663731,"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."}}