{"id":"W4313888578","doi":"10.3390/curroncol30010064","title":"Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography","year":2023,"lang":"en","type":"article","venue":"Current Oncology","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Artificial intelligence; Mammography; False positive paradox; Breast cancer; Support vector machine; Population; Gold standard (test); Machine learning; Digital mammography; Medical physics; Cancer; Radiology; Computer science; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001749168,0.0002219433,0.0003635059,0.0005574903,0.0001251547,0.00003326986,0.00009832527,0.00007041794,0.00001591904],"category_scores_gemma":[0.00003234067,0.000155592,0.0001647218,0.001157263,0.0001342939,0.00007042136,0.00006816623,0.0005316865,0.0001047038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005896077,"about_ca_system_score_gemma":0.00004896504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002301306,"about_ca_topic_score_gemma":0.00002901849,"domain_scores_codex":[0.9986756,0.00006394811,0.0001535279,0.0004077021,0.0002498628,0.0004493108],"domain_scores_gemma":[0.9993173,0.00007895465,0.00005763573,0.000197958,0.00006131562,0.0002868179],"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":[0.001021323,0.0003652039,0.4565284,0.00008120395,0.0001240966,0.0002203599,0.00029993,0.0001746939,0.00007494162,0.0005348036,0.002179579,0.5383955],"study_design_scores_gemma":[0.001553572,0.001519844,0.95074,0.0002188268,0.0001324206,0.001973968,0.0002232472,0.0007654895,0.00008931218,0.00009274008,0.0424141,0.0002764921],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853854,0.0003381268,0.0001385027,0.004071223,0.0003973408,0.0004197016,0.00002764334,0.0004048619,0.008817225],"genre_scores_gemma":[0.9988037,0.00002476953,0.0001567634,0.0002191425,0.0002062211,0.00003011608,0.0001845048,0.00002647978,0.0003483289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.538119,"threshold_uncertainty_score":0.6344858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07664421947553403,"score_gpt":0.3359154004739648,"score_spread":0.2592711809984308,"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."}}