Adaptive Cryoablation Control for Iceball Formation Precision
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Solution Overview
Problem
Existing cryoablation systems lack optimal performance and control due to standardized procedures that do not account for individual patient and treatment variations, leading to inefficiencies and potential damage to healthy tissues.
Innovation Solution
A cryoablation system with adaptive control using sensors and a computing device that adjusts operating parameters based on real-time data from sensors and patient information, employing a trained machine learning model to optimize ice formation and minimize tissue damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standardized procedures are used to control cryoablation systems, then the procedures can be determined by extensive testing using tissue analogues, but the system does not account for individual patient and treatment variations, leading to suboptimal performance
Solution Approach 1:
The system continuously monitors iceball formation characteristics during treatment and feeds this information back to the control algorithm, which adjusts operating parameters in real-time. This closed-loop feedback mechanism enables the system to adapt to individual patient variations while maintaining reliable and repeatable treatment outcomes through data-driven parameter optimization.
Solution Approach 2:
The control system transitions from static predetermined procedures to dynamic real-time adjustment of operating parameters. The machine learning algorithm continuously adapts parameters such as cryo-fluid flow rate and probe temperature based on live monitoring data, enabling the system to respond dynamically to individual patient anatomy and tissue properties while maintaining treatment reliability.
2Ease of operation
If predetermined procedures are used to control cryoablation parameters, then the system is easier to operate, but it cannot optimally control iceball formation for each individual treatment
Solution Approach 1:
The system performs self-optimization by automatically monitoring iceball formation and adjusting its own operating parameters without requiring manual intervention. The machine learning algorithm autonomously controls cryo-fluid flow rates and probe temperatures to achieve precise iceball formation, maintaining ease of operation while dramatically improving control precision through self-adjusting capabilities.
Solution Approach 2:
The system dynamically changes operating parameters such as cryo-fluid flow rate, probe temperature, and treatment duration based on real-time iceball formation monitoring. These parameter adjustments are automatically optimized by the control algorithm to achieve precise iceball formation for each individual treatment while maintaining simple operation through automated control.
3Reliability
If real-time monitoring and adjustment of operating parameters is implemented, then optimal control for individual treatments is achieved, but the device complexity increases
Solution Approach 1:
The control system integrates multiple functions into a single unified platform: monitoring iceball formation, analyzing patient-specific parameters, optimizing operating conditions, and controlling cryoablation delivery. This multi-functional integration achieves reliable and effective treatment for individual patients while managing device complexity through consolidated control architecture and standardized interfaces.
4Manufacturing precision
If extensive testing with tissue analogues is performed to finalize treatment procedures, then predetermined control parameters can be established, but the procedures cannot account for conditions that arise during individual treatments
Solution Approach 1:
The system performs preliminary characterization of patient-specific anatomy and tissue properties before treatment begins, using pre-acquired imaging and patient data. This preliminary action establishes baseline parameters that are then refined in real-time during treatment based on actual iceball formation monitoring, combining the benefits of pre-planned precision with adaptive responsiveness to emerging treatment conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the efficiency and repeatability of cryoablation treatments by dynamically adjusting parameters to match the ice formation plan, ensuring effective target tissue destruction while minimizing harm to surrounding tissues.
Implementation Method 1
Heat from the tissue passes from the tissue, through the probe, and into the fluid that removes heat from the targeted tissue
Implementation Method 2
This removal of heat causes tissue to freeze, resulting in the destruction of the targeted tissue
Data Source
AI summary
A system for performing cryoablation treatments includes at least one computing device that is configured to obtain cryoablation operating information from one or more sensors coupled to a cryoablation treatment apparatus and to obtain patient information from one or more patient monitoring apparatuses. The computing device also obtains ice formation information characterizing one or more characteristics of an iceball produced by the cryoablation treatment apparatus and compares the ice formation information to an ice formation plan. If the ice formation information differs from the ice formation plan, the computing device adjusts one or more operating parameters of the cryoablation treatment apparatus.