{"id":"W3203736792","doi":"10.2196/30066","title":"Deep Learning Techniques for Fatty Liver Using Multi-View Ultrasound Images Scanned by Different Scanners: Development and Validation Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Environment; National Research Foundation","keywords":"Fatty liver; Magnetic resonance imaging; Ultrasound; Artificial intelligence; Overfitting; Medicine; Liver disease; Radiology; Computer science; Artificial neural network; Pathology; Disease; 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":[0.0002153473,0.0002037019,0.0003648225,0.00006214023,0.0001863273,0.0000825501,0.00005916872,0.0001042489,0.0000905039],"category_scores_gemma":[0.0002218605,0.0001541388,0.00006783031,0.00009888113,0.00006342241,0.0001828457,0.00008167555,0.0001651219,0.000004517738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001627303,"about_ca_system_score_gemma":0.0001603937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001076063,"about_ca_topic_score_gemma":0.000007853127,"domain_scores_codex":[0.9984753,0.00005421193,0.0005294861,0.0001585919,0.0005368093,0.000245677],"domain_scores_gemma":[0.9990076,0.0001801827,0.0001413556,0.0001434411,0.0001788585,0.0003485043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001906525,0.01130121,0.5721157,0.004089851,0.001664739,0.0001899878,0.0356988,0.000006952625,0.001017304,0.00003143825,0.001423392,0.3722699],"study_design_scores_gemma":[0.0270188,0.00312332,0.5290249,0.00593671,0.003187549,0.0004357204,0.07228879,0.03069693,0.3131242,0.00003097628,0.01314319,0.00198896],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826428,0.0009281266,0.01460886,0.00009447678,0.00004119876,0.001535261,0.00001108322,0.00008676872,0.00005144277],"genre_scores_gemma":[0.9628047,0.0007064674,0.03493106,0.000446673,0.00005365632,0.0005039928,0.0004554135,0.00002379967,0.00007418735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.370281,"threshold_uncertainty_score":0.6285599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03133769379409989,"score_gpt":0.3289429064923638,"score_spread":0.2976052126982639,"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."}}