PFA Ablation Dosage Prediction for Durable Lesion Formation
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Solution Overview
Problem
Current ablation technologies, particularly Pulse Field Ablation (PFA), face challenges in determining dosage parameters that ensure durability, leading to frequent recurrences of atrial fibrillation despite advancements in electroanatomic mapping systems and catheters, which result in significant patient morbidity and healthcare resource utilization.
Innovation Solution
A system and method utilizing a processor and memory to implement a machine learning model trained on historical data, including physiological and dosage parameters, to determine optimal ablation settings for PFA, integrating multimodal neural networks and fuzzy set inferencing to predict procedure success and ensure durability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Pulse Field Ablation (PFA) is used to perform cardiac ablation, then tissue selectivity is improved and programed cell death is achieved, but the ability to determine dosage parameters for durability is worsened
Solution Approach 1:
The patent applies parameter changes by utilizing multiple physiological parameters (impedance, temperature, pressure, flow rate, oxygen saturation) to determine optimal ablation dosage. The system dynamically adjusts dosage parameters based on real-time physiological data and machine learning predictions, transforming the approach from static dosage selection to dynamic parameter optimization based on multiple measured variables
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring physiological parameters during the ablation procedure and using machine learning models to predict durability outcomes. The system provides real-time feedback on predicted durability and adjusts dosage parameters accordingly, creating a closed-loop control system that improves dosage determination accuracy despite the challenges of PFA-induced apoptosis
2Reliability
If machine learning models are used to determine dosage parameters, then durability prediction is improved, but device complexity is worsened
Solution Approach 1:
The patent applies universality by designing an integrated system where a single platform performs multiple functions: data acquisition from multiple sensors, machine learning model training and prediction, real-time dosage parameter determination, and durability assessment. This multi-functional approach consolidates complexity into a unified system rather than separate components, making the complex technology more manageable and clinically applicable
Solution Approach 2:
The patent uses machine learning models as intermediaries that bridge the gap between raw physiological data and clinical decision-making. The ML models process complex multi-parameter inputs and translate them into actionable durability predictions and dosage recommendations, serving as an intelligent mediator between the complex sensing system and the clinician's decision process
Data Source
AI summary
The present disclosure discloses systems and methods for determining dosage parameters to ensure durability in treatment processes. A system for determining dosage parameters to ensure durability in treatment processes may include at least a processor and a memory containing communicatively connected to the at least a processor. The memory may contain instructions configuring the processor to implement methods for determining dosage parameters to ensure durability in treatment processes. A method for determining dosage parameters to ensure durability in treatment processes may include receiving a plurality of historical data, training a machine learning model using the plurality of historical data, receiving current physiological data, determining dosage parameters using the current physiological data and the machine learning model, and initiating a treatment process using the dosage parameters.