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Procedures for Kidney StonesMedical intervention is necessary when kidney stones or renal calculi are too large to pass spontaneously (typically greater than 5 millimeters) when stones are accompanied by symptomatic infection (such as fever or pyelonephritis), when they impair kidney function, or when they cause persistent symptoms like severe pain, nausea, or urinary retention. Additionally, patients with only one kidney or those who cannot be treated with medical management also require...
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Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning.

Zhipeng Chen1, Daniel D Zeng2, Ryan G N Seltzer3

  • 1Shenzhen Artificial Intelligence and Data Science Institute (Longhua), Longhua, Shenzhen, China.

JMIR Medical Informatics
|May 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for personalized shock wave lithotripsy (SWL) treatment planning, improving consistency and reducing risks. The AI demonstrates capabilities comparable to expert physicians in optimizing SWL procedures.

Keywords:
artificial intelligencedeep learningextracorporeal shock wave therapylithotripsynephrolithiasistreatment planning

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Area of Science:

  • Nephrology
  • Artificial Intelligence
  • Medical Device Technology

Background:

  • Shock wave lithotripsy (SWL) is a common treatment for kidney stones (nephrolithiasis).
  • Current SWL treatment planning relies on subjective physician judgment, leading to variable quality and potential risks.
  • Physician inexperience can negatively impact SWL outcomes.

Purpose of the Study:

  • To develop a deep learning model for automated, step-by-step SWL treatment planning.
  • To generate personalized SWL treatment protocols based on patient characteristics and previous treatment data.
  • To improve the quality and consistency of SWL treatments.

Main Methods:

  • A deep learning model using long short-term memory (LSTM) networks was developed.
  • The model predicts optimal power level, shock rate, and number of shocks for the next treatment step.
  • The model was trained on 8583 SWL treatment steps from the International Stone Registry and validated against baseline models.

Main Results:

  • The deep learning model significantly outperformed baseline models in predicting treatment parameters.
  • Model accuracy reached 98.8% for power levels and 98.1% for shock rates.
  • No significant difference was found between the AI-generated treatment steps and those from top physicians (P > .480).

Conclusions:

  • The deep learning approach demonstrates treatment planning capabilities on par with expert physicians.
  • This framework represents the first automated planning of SWL treatment using deep learning.
  • The AI offers a promising, low-cost method for assisting SWL treatment planning and physician training.