{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009257742,0.00056047,0.0003717005,0.0005694542,0.0002328369,0.0005980956,0.0009086429,0.0008404367,0.002168288],"category_scores_gemma":[0.003655264,0.0004799803,0.0007184223,0.0003912974,0.0004253713,0.0006428699,0.0007198272,0.001470659,0.0005445046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00161131,"about_ca_system_score_gemma":0.002119466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008865965,"about_ca_topic_score_gemma":0.01075279,"domain_scores_codex":[0.9995964,0.0001106087,0.00002765242,0.0001294444,0.0000944989,0.00004136324],"domain_scores_gemma":[0.9987135,0.0007396084,0.0001854816,0.0001232333,0.0001852913,0.00005298827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001395657,0.0001266605,0.005509425,0.0001156578,0.00005227319,0.00009245153,0.00005520249,0.844734,0.00335171,0.0008693292,0.002957694,0.141996],"study_design_scores_gemma":[0.00001633792,0.00003559845,0.0006771117,0.00001441955,0.000009109341,0.00003608109,0.000008650861,0.9937033,0.003028485,0.001699996,0.0007625812,0.000008378627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09252592,0.0006987364,0.8988959,0.0009995325,0.00005177923,0.0002268863,0.0009154661,0.003483329,0.002202475],"genre_scores_gemma":[0.7393888,0.0004050181,0.2561317,0.0003270819,0.00002967139,0.000313731,0.001427717,0.0001579763,0.001818164],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008865965,"threshold_uncertainty_score":0.01762867,"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."}}