Tissue Susceptibility Prediction for Reliable Pulse Field Ablation
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Solution Overview
Problem
Pulse field ablation procedures are unreliable due to challenges in determining if targeted tissue has been fully ablated, leading to potential recurrence of symptoms and the need for an individualized approach considering tissue susceptibility variations.
Innovation Solution
An apparatus and method using an imaging device, processor, and memory to generate a tissue susceptibility prediction model, which analyzes pre-ablation images and generates a susceptibility value for tissue ablation, displayed through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pulse field ablation is performed without tissue susceptibility assessment, then the procedure is simpler and faster, but the reliability of complete tissue ablation is reduced
Solution Approach 1:
The system performs preliminary imaging and susceptibility assessment before the ablation procedure to predict tissue response. This advance preparation allows clinicians to plan the ablation strategy based on predicted susceptibility values, ensuring more reliable complete ablation while maintaining procedural efficiency through pre-computed predictions.
Solution Approach 2:
The system provides feedback on tissue susceptibility values during the ablation procedure, allowing real-time adjustment of ablation parameters. This feedback mechanism ensures that the ablation is tailored to the actual tissue characteristics, improving reliability of complete ablation while the automated nature of the feedback minimizes additional complexity.
2Adaptability or versatility
If a standardized ablation approach is used, then the procedure is easier to perform, but it fails to account for individual tissue susceptibility variations
Solution Approach 1:
The system dynamically adapts the ablation approach based on individually predicted tissue susceptibility values. The ablation parameters are adjusted in real-time according to the specific tissue characteristics of each patient and even different regions within the same tissue, enabling personalized treatment while the automated calculation maintains ease of operation.
Solution Approach 2:
The system applies different ablation strategies to different regions of tissue based on locally predicted susceptibility values. Each region is treated according to its specific characteristics rather than applying a uniform approach, achieving individualization while the automated regional analysis simplifies the operational complexity.
3Manufacturing precision
If tissue susceptibility is not assessed, then the procedure requires less time and resources, but insufficient ablation may occur leading to symptom recurrence
Solution Approach 1:
The susceptibility assessment is performed as a preliminary step using pre-existing imaging data, avoiding the need for additional time-consuming procedures during the ablation. The predictive model is trained in advance on historical data, and susceptibility values are computed quickly before the procedure, enabling precise ablation planning without significant time loss.
Solution Approach 2:
The system uses computational models and simulations to predict tissue susceptibility without requiring physical testing or additional invasive procedures. Virtual assessments based on imaging data provide the necessary precision information without adding substantial time to the overall procedure.
Data Source
AI summary
An apparatus and method for determining a tissue susceptibility value from medical data. The apparatus includes an imaging device, wherein the imaging device is configured to detect an image of a subject, wherein the subject receives tissue ablation, a memory, and at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to receive the image of the subject, generate a tissue susceptibility prediction model, wherein the tissue susceptibility prediction model is configured to receive, as input, the image of the subject, and determine a tissue susceptibility value as a function of the image, wherein the tissue susceptibility value defines a susceptibility of a tissue to the tissue ablation, display, using a downstream device, the tissue susceptibility value through a graphical user interface.