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A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

Historically, shock wave lithotripsy (SWL) has been the most used procedure for treating upper tract urolithiasis and stones smaller than 20 mm in diameter due to its noninvasive nature, lower cost, fewer side effects, and faster recovery [3,4]. In Ontario, Canada, SWL is a regionalized and limited resource. St. Michael’s Hospital in downtown Toronto is one of the only 3 centers in the province offering this service.

Reihaneh Moghisi, Christo El Morr, Kenneth T Pace, Mohammad Hajiha, Jimmy Huang

Interact J Med Res 2022;11(1):e33357

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

Shock wave lithotripsy (SWL, or extracorporeal shock wave lithotripsy) has been considered as a safe and effective noninvasive treatment option for nephrolithiasis since its introduction in early 1980s [1]. Reported SWL stone-free rates approach 74%-88% [2,3]; however, it is not without risk. Common contraindications to SWL include pregnancy, coagulopathy or use of platelet aggregation inhibitors, aortic aneurysms, severe untreated hypertension, and untreated urinary tract infections [4].

Zhipeng Chen, Daniel D Zeng, Ryan G N Seltzer, Blake D Hamilton

JMIR Med Inform 2021;9(5):e24721