Advanced Catheter Navigation System with AI-Driven Visualization
Legal Citation
Summary of the Inventive Concept
A next-generation catheter navigation system that leverages artificial intelligence, machine learning, and advanced visualization techniques to provide real-time guidance and enhance navigation accuracy in minimally invasive procedures.
Background and Problem Solved
The original patent disclosed a system for displaying an alignment CT, which, while effective, has limitations in terms of accuracy and real-time feedback. The new inventive concept addresses these limitations by introducing AI-driven visualization and machine learning-based segmentation to provide more accurate and personalized navigation.
Detailed Description of the Inventive Concept
The advanced catheter navigation system comprises a neural network trained on CT image data to predict optimal catheter trajectories, a visualization module to display the predicted trajectories in real-time, and a hybrid rendering engine utilizing both Maximum Intensity Projection (MIP) and Volume Rendering (VR) techniques to provide an unobstructed view of the target region. The system also incorporates real-time catheter tracking using augmented reality (AR) visualization to project the catheter's position and trajectory onto the patient's body. Furthermore, the system generates personalized 3D models of patient airways from CT image data using a generative adversarial network (GAN) to refine the 3D model and improve navigation accuracy.
Novelty and Inventive Step
The new claims introduce novel AI-driven visualization and machine learning-based segmentation techniques that significantly improve navigation accuracy and provide real-time feedback. The use of a neural network to predict optimal catheter trajectories, the hybrid rendering engine, and the incorporation of AR visualization are all non-obvious and innovative steps beyond the original patent.
Alternative Embodiments and Variations
Alternative embodiments of the inventive concept could include the use of other machine learning algorithms, such as deep learning or reinforcement learning, to improve navigation accuracy. Additionally, the system could be adapted for use in other minimally invasive procedures, such as cardiovascular or neurosurgical interventions.
Potential Commercial Applications and Market
The advanced catheter navigation system has significant commercial potential in the medical device industry, particularly in the fields of pulmonology, cardiology, and neurosurgery. The system's ability to provide real-time guidance and enhance navigation accuracy could lead to improved patient outcomes, reduced procedure times, and increased adoption of minimally invasive procedures.
CPC Classifications
| Section | Class | Group |
|---|---|---|
| A | A61 | A61B34/20 |
| A | A61 | A61B34/25 |
| A | A61 | A61B90/37 |
| G | G06 | G06T15/00 |
| A | A61 | A61B1/018 |
| A | A61 | A61B1/2676 |
| A | A61 | A61B10/02 |
| A | A61 | A61B2017/00809 |
| A | A61 | A61B2034/107 |
| A | A61 | A61B2034/2051 |
| A | A61 | A61B2034/2055 |
| A | A61 | A61B2034/2061 |
| A | A61 | A61B2034/2065 |
| A | A61 | A61B2034/2068 |
| A | A61 | A61B2034/2072 |
| A | A61 | A61B2090/367 |
| A | A61 | A61B2090/3925 |
| A | A61 | A61M2025/0166 |
Section 103 Obviousness Analysis (PHOSITA)
Field of Art
Medical imaging and interventional navigation systems, specifically focusing on CT-guided catheter navigation and visualization techniques in minimally invasive medical procedures. Requires expertise in medical imaging, computer vision, 3D rendering, and medical device navigation technologies
Person of Ordinary Skill (PHOSITA) Profile
A biomedical engineer or medical imaging specialist with advanced degrees in bioengineering, computer science, or medical technology, possessing expertise in medical image processing, machine learning, 3D visualization techniques, and interventional medical device design
Obviousness Rationale
A PHOSITA would recognize that the advanced AI-driven visualization and navigation techniques represent predictable extensions of existing medical imaging technologies. The proposed machine learning and augmented reality approaches are logical progressions of known medical imaging and navigation methodologies, utilizing standard computational techniques to enhance existing catheter navigation systems. The incremental improvements in trajectory prediction, rendering techniques, and real-time visualization would be considered obvious refinements to a skilled practitioner in the field.
Obvious Combinations & Variations
Original Patent Information
| Patent Number | US 11,857,276 |
|---|---|
| Title | System and method for displaying an alignment CT |
| Assignee(s) | Covidien LP |