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

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.

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

“…[ 51 ] Optimization of ESWL protocol Experimental AUC of 0.838 in the prediction of fragmentation with less than 72 shockwaves Other multivariate models with lower performance Chen et al. [ 52 ] Optimization of ESWL protocol Cross-sectional Prediction accuracy values for power level, shockwave rate of 98.8%, 98.1%, respectively Other multivariate models with lower performance Muller et al. [ 53 ] Optimization of ESWL protocol Cross-sectional Shockwave hit rate of 75.3% Shockwave hit rate of 55.2% Taguchi et al.…”

“…In a recent report, a DL model was constructed and trained to produce personalized ESWL protocols, which included the ESWL settings (power lever, shockwave rate, and total number) for each of the steps of every ESWL session [ 52 ]. The data used for the model were extracted from the best practices of ESWL treatments recorded in the International Stone Registry.…”

Artificial intelligence in endourology: emerging technology for individualized care

“…Chen et al further developed a deep learning model to predict individualized SWL treatment plans. Using preoperative patient data from 1216 SWL cases performed by 54 surgeons with the top quartile of treatment success rates in the International Stone Registry, it predicted treatment parameters with an accuracy of 98.8% for power level, 98.1% for shock rate, and root mean square error of 207 for shock rate [44 ▪▪ ]. It also performed similarly to treatment plans by expert surgeons, thus introducing a framework for automated SWL treatment planning.…”

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

“…These models learn from data and experience, enabling them to make predictions, recognize patterns, and solve problems without being explicitly programmed for each specific task. They are now widely used in urology to detect kidney stones in videos [ 18 ] and images [ 19 – 24 ], predict sepsis risk [ 25 , 26 ] and lithotripsy treatment outcomes [ 27 – 29 ], and set SWL machine parameters [ 30 ].…”