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

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

doi:10.2196/24721

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

JMIR Med Inform. 2021 May 11;9(5):e24721. doi: 10.2196/24721.

Authors

Zhipeng Chen¹, Daniel D Zeng², Ryan G N Seltzer³, Blake D Hamilton⁴

Affiliations

¹ Shenzhen Artificial Intelligence and Data Science Institute (Longhua), Longhua, Shenzhen, China.
² The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
³ Translational Analytics and Statistics, Tucson, AZ, United States.
⁴ School of Medicine, University of Utah, Salt Lake City, UT, United States.

PMID: 33973862
PMCID: PMC8150413
DOI: 10.2196/24721

Abstract

Background: Though shock wave lithotripsy (SWL) has developed to be one of the most common treatment approaches for nephrolithiasis in recent decades, its treatment planning is often a trial-and-error process based on physicians' subjective judgement. Physicians' inexperience with this modality can lead to low-quality treatment and unnecessary risks to patients.

Objective: To improve the quality and consistency of shock wave lithotripsy treatment, we aimed to develop a deep learning model for generating the next treatment step by previous steps and preoperative patient characteristics and to produce personalized SWL treatment plans in a step-by-step protocol based on the deep learning model.

Methods: We developed a deep learning model to generate the optimal power level, shock rate, and number of shocks in the next step, given previous treatment steps encoded by long short-term memory neural networks and preoperative patient characteristics. We constructed a next-step data set (N=8583) from top practices of renal SWL treatments recorded in the International Stone Registry. Then, we trained the deep learning model and baseline models (linear regression, logistic regression, random forest, and support vector machine) with 90% of the samples and validated them with the remaining samples.

Results: The deep learning models for generating the next treatment steps outperformed the baseline models (accuracy = 98.8%, F1 = 98.0% for power levels; accuracy = 98.1%, F1 = 96.0% for shock rates; root mean squared error = 207, mean absolute error = 121 for numbers of shocks). The hypothesis testing showed no significant difference between steps generated by our model and the top practices (P=.480 for power levels; P=.782 for shock rates; P=.727 for numbers of shocks).

Conclusions: The high performance of our deep learning approach shows its treatment planning capability on par with top physicians. To the best of our knowledge, our framework is the first effort to implement automated planning of SWL treatment via deep learning. It is a promising technique in assisting treatment planning and physician training at low cost.

Keywords: artificial intelligence; deep learning; extracorporeal shock wave therapy; lithotripsy; nephrolithiasis; treatment planning.

©Zhipeng Chen, Daniel D Zeng, Ryan G N Seltzer, Blake D Hamilton. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 11.05.2021.