{"id":"W3162291681","doi":"10.2196/24721","title":"Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Deep learning; Random forest; Machine learning; Logistic regression; Computer science; Mean squared error; Shock wave lithotripsy; Artificial neural network; Medicine; Lithotripsy; Statistics; Surgery; Mathematics","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":[0.0001901068,0.0002110772,0.0005251023,0.0001014392,0.0001180994,0.00003013106,0.00004945365,0.0002349275,0.0003593036],"category_scores_gemma":[0.0002762683,0.0001567442,0.0001577543,0.0002691064,0.00008108148,0.0001399055,0.00004469357,0.0002214518,0.0000121432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002316209,"about_ca_system_score_gemma":0.0006076669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001116697,"about_ca_topic_score_gemma":5.779831e-7,"domain_scores_codex":[0.9979218,0.00007842944,0.0007966232,0.0001292372,0.0007871649,0.0002867991],"domain_scores_gemma":[0.9988466,0.00005669433,0.0002780622,0.0002221072,0.0002105562,0.000385964],"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":[0.002982051,0.01274808,0.2907535,0.009436452,0.006602339,0.004546603,0.2756411,0.003367493,0.0244547,0.001458673,0.005004932,0.3630041],"study_design_scores_gemma":[0.007073531,0.0008231827,0.003472893,0.0008622865,0.0002235709,0.0008766147,0.00368783,0.9637847,0.005050621,0.000004752775,0.01395329,0.0001867345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916826,0.0002290951,0.001892299,0.0001955652,0.00008411248,0.003889054,0.000007862796,0.0002334162,0.001786023],"genre_scores_gemma":[0.9236485,0.0001570932,0.06753925,0.001632511,0.0005979017,0.003247374,0.001101652,0.00008627027,0.001989399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9604172,"threshold_uncertainty_score":0.6391843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09310149983732104,"score_gpt":0.3906213265459849,"score_spread":0.2975198267086639,"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."}}