Kidney Stone Targeting via Respiration Prediction Model
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Solution Overview
Problem
Existing extracorporeal shock wave lithotripsy (ESWL) techniques face challenges in accurately targeting kidney stones due to patient respiration-induced displacement, leading to compromised treatment precision and potential tissue damage.
Innovation Solution
A calculus targeting method that includes a sampling step, model establishing step, prediction step, and firing determination step, utilizing a motion function and direction and time correlation table to predict stone location and adjust weights for accurate shock wave delivery, while incorporating error analysis and correction for improved precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If shock waves are focused on a calculus without real-time tracking, then the treatment process is simple, but the precision of calculus targeting deteriorates due to respiration-induced displacement
Solution Approach 1:
The system performs preliminary actions by acquiring calculus coordinates at multiple time points before treatment, establishing a time-location correlation table and prediction model in advance. This allows the system to predict calculus position at future time points, enabling precise targeting while maintaining operational simplicity.
Solution Approach 2:
The system implements feedback by continuously monitoring calculus position through coordinate acquisition at different time points, comparing actual positions with predicted positions, and adjusting the prediction model accordingly. This feedback mechanism ensures high targeting precision while automating the process to maintain simplicity.
2Measurement precision
If real-time calculus tracking is implemented to improve targeting precision, then the precision of shock wave delivery is improved, but the system complexity increases due to multiple modules and calculations
Solution Approach 1:
The prediction model serves multiple functions: it predicts calculus position at future time points, validates prediction accuracy by comparing with actual coordinates, and adapts to individual patient respiration patterns. This multi-functionality reduces the need for separate systems, thereby managing complexity while achieving high precision.
Solution Approach 2:
The system changes parameters by establishing correlation between time and calculus location, using this correlation to predict future positions. The model adapts parameters based on individual patient data, achieving high precision without requiring complex real-time intervention systems.
3Measurement precision
If prediction models are established based on individual patient respiration patterns, then the accuracy of calculus location prediction is improved, but the time required for sampling and model establishment increases
Solution Approach 1:
The system performs preliminary sampling of calculus coordinates at multiple time points during a sampling period to establish the time-location correlation table and prediction model before actual treatment begins. This preliminary action allows the model to be ready for rapid, accurate predictions during treatment without adding time to the procedure.
Solution Approach 2:
The prediction model is dynamic and adapts to individual patient respiration patterns. By learning from the sampled data specific to each patient's breathing characteristics, the model achieves high accuracy predictions tailored to that patient without requiring extended sampling periods beyond normal respiratory cycles.
4Reliability
If multiple sets of predicted coordinates with different weights are used, then the reliability of calculus targeting is improved, but the complexity of coordinate processing and weight adjustment increases
Solution Approach 1:
The system uses feedback by comparing predicted calculus coordinates with actual coordinates obtained during treatment. Based on this comparison, the system adjusts the weights of different prediction methods and refines the prediction model, thereby improving reliability while automating the complex processing through adaptive algorithms.
Solution Approach 2:
The system dynamically adjusts parameters including the weights of different predicted coordinate sets based on prediction accuracy and actual performance. This parameter adaptation allows the system to optimize targeting reliability automatically, managing processing complexity through intelligent parameter tuning rather than fixed complex procedures.
Data Source
AI summary
A calculus targeting system for performing a calculus targeting method includes a processor, a storage unit, a calculus coordinate acquisition module, a model establishing module, a prediction module, a firing determination module, and a shock wave module, all the latter six of which are connected to the processor. The calculus targeting method can be programmed as a computer program product and essentially includes a sampling step for acquiring different sets of calculus coordinates in a sampled respiration cycle, a model establishing step for establishing a prediction model, and a firing determination step for determining according to the prediction model whether to fire or not.