Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24721, first published .
Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning

Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning

Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning

Journals

  1. Xiaqiu L. Information Automatic Sharing Model Based on Big Data Mining Algorithm. Journal of Physics: Conference Series 2021;1982(1):012137 View
  2. Dai J, Johnson B. Artificial intelligence in endourology: emerging technology for individualized care. Current Opinion in Urology 2022;32(4):379 View
  3. Bouhadana D, Lu X, Luo J, Assad A, Deyirmendjian C, Guennoun A, Nguyen D, Kwong J, Chughtai B, Elterman D, Zorn K, Trinh Q, Bhojani N. Clinical Applications of Machine Learning for Urolithiasis and Benign Prostatic Hyperplasia: A Systematic Review. Journal of Endourology 2023;37(4):474 View
  4. Anastasiadis A, Koudonas A, Langas G, Tsiakaras S, Memmos D, Mykoniatis I, Symeonidis E, Tsiptsios D, Savvides E, Vakalopoulos I, Dimitriadis G, de la Rosette J. Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review. Asian Journal of Urology 2023;10(3):258 View
  5. Noble P, Hamilton B, Gerber G, Eissa A. Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients. PLOS ONE 2024;19(5):e0301812 View

Books/Policy Documents

  1. Shirley C, Napshala Joshi A, Gokula Lakshmi G. Soft Computing for Security Applications. View