{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000917018,0.0006252421,0.0004020933,0.001326115,0.0002229778,0.0004165977,0.0007445776,0.0008244979,0.001103949],"category_scores_gemma":[0.001722196,0.0002289843,0.0004928013,0.0005338246,0.000248763,0.0005535561,0.0005121995,0.0007288848,0.0003132507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008765842,"about_ca_system_score_gemma":0.00090212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007478093,"about_ca_topic_score_gemma":0.00604958,"domain_scores_codex":[0.9996754,0.00009136368,0.00001991449,0.00007383554,0.00007536881,0.00006416937],"domain_scores_gemma":[0.9993896,0.0002414456,0.00007455188,0.00004039658,0.0002110488,0.00004291529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003508906,0.0003912922,0.0238552,0.00009994345,0.0001129084,0.000314688,0.00008762021,0.3538968,0.01265239,0.001802892,0.005494848,0.6009406],"study_design_scores_gemma":[0.0000055589,0.00003798362,0.001793476,0.000006510576,0.000009437187,0.00005019598,0.00001163807,0.9950937,0.002011106,0.0007566104,0.000218376,0.000005361933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.427979,0.001832526,0.560879,0.0009538975,0.00008240966,0.0001590405,0.0005923336,0.00332723,0.004194616],"genre_scores_gemma":[0.9084067,0.0003291065,0.08862628,0.0001767276,0.00003760251,0.00007220048,0.0005141834,0.00003831454,0.001798968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007478093,"threshold_uncertainty_score":0.01486909,"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."}}