{"id":"W4308053321","doi":"10.2196/40878","title":"Automatic Screening of Pediatric Renal Ultrasound Abnormalities: Deep Learning and Transfer Learning Approach","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pediatric Urology and Nephrology Studies","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Taichung Veterans General Hospital; Ministry of Science and Technology, Taiwan","keywords":"Artificial intelligence; Receiver operating characteristic; Deep learning; Preprocessor; Computer science; Transfer of learning; Ultrasound; Hydronephrosis; Data set; Test set; Pattern recognition (psychology); Medicine; Radiology; Machine learning","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":[0.001183063,0.0001970962,0.0005282849,0.0002841014,0.0005093984,0.000008099168,0.0001436216,0.0002087827,0.0008483627],"category_scores_gemma":[0.0004219259,0.0001723327,0.0001030705,0.0004075295,0.0003615686,0.0001312238,0.0001859116,0.001772452,0.000004512719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002630201,"about_ca_system_score_gemma":0.0001337711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005226028,"about_ca_topic_score_gemma":7.237405e-7,"domain_scores_codex":[0.9975987,0.0002052693,0.0008039139,0.0001360624,0.0008474251,0.000408625],"domain_scores_gemma":[0.9987351,0.000684689,0.0001763021,0.0001165674,0.00005592241,0.0002313734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001206829,0.001238722,0.7305205,0.003707687,0.002545305,0.0002253665,0.187726,0.002057347,0.00006898354,0.00106693,0.0043474,0.0652889],"study_design_scores_gemma":[0.02735923,0.01192463,0.2301995,0.0001222429,0.005208029,0.01080496,0.1680434,0.4802221,0.00007199231,0.0002699946,0.06360063,0.002173259],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903627,0.001332448,0.003758107,0.0001554562,0.00005942268,0.0003029684,0.000003974503,0.0001426453,0.003882262],"genre_scores_gemma":[0.9950567,0.0009501235,0.00254669,0.0007998986,0.000216234,0.0001213589,0.0000663629,0.00001806209,0.0002245768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.500321,"threshold_uncertainty_score":0.9288977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331631888609758,"score_gpt":0.2635820558924516,"score_spread":0.250265737006354,"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."}}