Conduction System Pacing Lead Placement With ML Imaging Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for implanting medical leads to achieve conduction system pacing in the heart are inefficient due to difficulties in accurately determining the optimal electrode placement within the heart, often relying on two-dimensional imaging and manual assessment which can be misleading and time-consuming.
Innovation Solution
A medical system utilizing an imaging device, processing circuitry, and a machine learning algorithm to generate an output indicating the likelihood of successful conduction system pacing based on electrode position data, trained with a dataset of past procedures, enabling precise electrode placement within the heart.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two-dimensional imaging and manual assessment are used to determine electrode placement, then the procedure is simpler to perform, but the accuracy of electrode placement is reduced
Solution Approach 1:
The patent replaces manual visual assessment and two-dimensional imaging with a machine learning-based automated analysis system. The machine learning algorithm processes imaging data to predict conduction system pacing likelihood, substituting the mechanical/manual process with an intelligent automated system that provides three-dimensional spatial understanding and quantitative predictions without requiring complex additional hardware.
2Productivity
If traditional manual methods are used for electrode placement assessment, then the procedural time is shorter for setup, but the overall procedural efficiency is reduced due to trial-and-error placement
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning algorithm continuously provides predictions about conduction system pacing likelihood based on electrode position. This feedback loop allows operators to adjust electrode placement in real-time based on predicted outcomes, eliminating trial-and-error approaches and reducing the time needed to achieve successful pacing while improving overall procedural efficiency.
3Measurement precision
If three-dimensional imaging and machine learning analysis are implemented, then the electrode placement accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent achieves three-dimensional spatial understanding and machine learning analysis by integrating these functions into an existing imaging system framework. The system processes imaging data through machine learning algorithms that provide multiple functions: three-dimensional reconstruction, electrode position tracking, and pacing likelihood prediction, all within a unified platform that leverages existing hardware capabilities.
4Reliability
If manual assessment methods are used, then the system is easier to operate, but the reliability of conduction system pacing prediction is reduced
Solution Approach 1:
The patent implements a self-service mechanism where the machine learning algorithm autonomously analyzes imaging data and provides predictions about conduction system pacing likelihood without requiring manual interpretation. The system automatically processes the data, identifies relevant features, and generates reliable predictions, reducing operator burden while maintaining or improving reliability through consistent algorithmic analysis.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A medical system including processing circuitry configured to operably couple to an imaging device configured to generate an image of an apparatus within a heart of a patient. The apparatus may be configured to establish conduction system pacing (CSP) of the heart using the electrode. The processing circuitry is configured to receive image data representative of the generated image from the imaging device and generate an output readable by a clinician indicating a likelihood of successful conduction system pacing based on electrode position data indicative of a position of the electrode within the heart. The processing circuitry is configured to determine the likelihood of success using a machine learning algorithm trained with a training data set indicative of successful electrode placements within an anatomical heart.