Cardiac Mapping Catheter With ML Guidance for Rhythm Ablation
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Solution Overview
Problem
Conventional diagnosis and treatment of biological rhythm disorders require multiple tools and tools exchange during procedures, leading to inefficiencies, errors in positioning, and reduced procedural success due to rule-based analytical algorithms prone to mischaracterizing electrical signals.
Innovation Solution
A system integrating a single catheter with computer-based software and visualization display for interactive feedback, using machine-learning algorithms to analyze electrical signals, guide therapy, and deliver treatment to critical regions, enhancing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple tools are used for diagnosis and treatment, then comprehensive functionality is achieved, but procedural time and complexity increase
Solution Approach 1:
The patent combines diagnostic sensing capabilities and therapeutic delivery capabilities into a single integrated catheter system. The dual-purpose catheter includes both sensing electrodes for electrical signal acquisition and therapy delivery elements, eliminating the need to exchange between separate diagnostic and therapeutic tools during the procedure.
Solution Approach 2:
The catheter is designed with multi-functionality to perform both diagnosis and treatment operations. The same device can sense electrical signals, map activation waves, identify critical regions, and deliver ablation therapy, making it a universal tool for managing biological rhythm disorders.
2Adaptability or versatility
If multiple tools are exchanged during procedure, then comprehensive coverage is achieved, but positioning accuracy decreases
Solution Approach 1:
By merging diagnostic and therapeutic functions into one catheter, the system maintains continuous positioning context throughout the procedure. The catheter remains in the same anatomical location while performing both sensing and therapy delivery, eliminating positioning errors that would occur during tool exchanges.
3Device complexity
If rule-based algorithms are used for signal analysis, then computational simplicity is maintained, but diagnostic accuracy decreases
Solution Approach 1:
The patent replaces traditional rule-based analytical algorithms with machine learning models that automatically learn patterns from electrical signals. The activation detection model and wave clustering algorithms use neural networks to identify critical regions, improving diagnostic accuracy while the system architecture remains computationally manageable through optimized learning models.
Data Source
AI summary
A heart treatment system is disclosed capable of guiding a device towards one or more critical sites of interest by sensing signals from tissue. If a critical site is not present at the current location of sensed signals, the system is capable of indicating a guidance direction in which to navigate to reach one or more critical sites. When stopping rules for direction are met, treatment can be applied to said region of interest by thermal or non-thermal energy delivery. Signals are again sensed and analyzed to assess the impact of treatment. This process is repeated until all critical sites of interest are treated. In some embodiments, all functionality is provided by a single sensing and treating device coupled with a display device and analytical software.