JMIR Publications

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 shock number of 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 = 8,583) 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% samples, and validated them with the remaining samples.

Results:

The deep learning models for generating 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 shock numbers). The hypothesis testing shows 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 shock numbers).

Conclusions:

The high performance of our deep learning approach shows its treatment planning capability on a par with top physicians. To our best 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 costs.