XR Pulmonary Guidance With Motion Compensation for Catheter Placement
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Solution Overview
Problem
Current pulmonary procedures, such as targeted lung denervation for COPD, face challenges due to the lack of integrated data compilation and AI/ML implementation, leading to cognitive overload, inaccurate catheter placement, and complications from separate imaging and surgical tool usage.
Innovation Solution
An intelligent assistance ecosystem utilizing extended reality (XR) systems that integrate surgical tool, imaging, and patient data, providing real-time 3D/4D guided navigation and control inputs to medical professionals, compensating for patient movements and tissue distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional separate imaging and surgical tool usage is employed, then procedural simplicity is maintained, but manufacturing precision and measurement precision deteriorate due to inaccurate catheter placement
Solution Approach 1:
The patent combines separate imaging systems and surgical tools into an integrated intelligent assistance ecosystem. The system merges fluoroscopy imaging data, 3D anatomical models, and catheter navigation tools into a unified platform that provides real-time guidance, thereby improving catheter placement accuracy while managing system complexity through integrated architecture.
Solution Approach 2:
The patent replaces traditional mechanical navigation methods with AI/ML-based intelligent guidance systems. Machine learning algorithms analyze imaging data and provide automated catheter placement recommendations, substituting manual mechanical navigation with intelligent computational guidance to enhance precision.
2Measurement precision
If comprehensive data compilation and AI/ML implementation are employed, then manufacturing precision improves through real-time guidance, but device complexity and loss of information increase
Solution Approach 1:
The patent extracts and processes only the most critical data elements from comprehensive medical data sets using AI/ML algorithms. The system identifies and focuses on key imaging features and anatomical landmarks relevant to catheter placement, filtering out redundant information to maintain measurement precision while reducing data processing overhead.
Solution Approach 2:
The patent segments the comprehensive data compilation into distinct functional modules: imaging data acquisition, 3D model generation, AI/ML analysis, and guidance output. This segmentation allows efficient processing of large data sets by dividing them into manageable components, reducing information loss while maintaining real-time guidance accuracy.
3Productivity
If integrated data compilation and real-time guidance are implemented, then productivity improves through reduced cognitive load, but device complexity increases
Solution Approach 1:
The patent implements self-service functionality where the intelligent assistance ecosystem automatically performs data compilation, analysis, and guidance generation without requiring manual intervention. The system autonomously processes imaging data, generates 3D models, and provides real-time catheter placement recommendations, thereby improving procedural efficiency while managing complexity through automation.
Solution Approach 2:
The patent designs the intelligent assistance ecosystem to perform multiple functions: imaging acquisition, 3D reconstruction, AI/ML analysis, navigation guidance, and procedural documentation. This multi-functionality consolidates what would otherwise require separate systems into a single unified platform, improving productivity while containing overall system complexity.
Data Source
AI summary
Novel tools and techniques are provided for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for pulmonary procedures. In various embodiments, a computing system might analyze received one or more first layer input data (i.e., room content-based data) and received one or more second layer input data (i.e., patient and/or tool-based data), and might generate one or more recommendations for guiding a medical professional in guiding a surgical device(s) toward and within a lung of the patient to perform a pulmonary procedure, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the lung of the patient. The computing system might then generate one or more XR images, based at least in part on the generated one or more recommendations, and might present the generated one or more XR images using a UX device.