AI-Based Endoscopic Tissue Acquisition Planning for Deep Anatomical Access
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Solution Overview
Problem
Conventional endoscopes face challenges in navigating to deep anatomical regions within patients, particularly in ERCP procedures, due to limited maneuverability and the need for advanced surgical skills, especially in patients with altered anatomy, and lack automated tissue acquisition planning.
Innovation Solution
An AI-based endoscopic system with a steerable elongate instrument and processor uses machine learning to plan and automate tissue acquisition, recommending tools and operational parameters for efficient tissue collection, enhancing navigation and reducing reliance on manual skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional endoscopes are used for navigating deep anatomical regions, then the procedure can be performed with existing technology, but the maneuverability is limited and advanced surgical skills are required
Solution Approach 1:
The patent replaces manual mechanical navigation with an AI-based system that automatically plans and guides the endoscope to deep anatomical regions. The machine learning model processes anatomical images and generates navigation paths, substituting the need for advanced manual surgical skills with automated computational guidance.
Solution Approach 2:
The AI system performs self-guided navigation by automatically analyzing anatomical structures and determining optimal paths without requiring extensive operator intervention or expertise. The system serves itself by making autonomous decisions about navigation and tissue acquisition planning.
2Manufacturing precision
If manual tissue acquisition planning is used, then the procedure follows conventional workflow, but there is variability among operators and inadequate tissue collection may occur
Solution Approach 1:
The AI system provides real-time feedback by analyzing anatomical images and automatically adjusting the tissue acquisition plan based on the visualized structures. The system continuously monitors the procedure and refines its recommendations, ensuring consistent and accurate tissue collection regardless of operator variability.
Solution Approach 2:
The patent replaces manual tissue acquisition planning with an automated AI system that uses machine learning to determine optimal biopsy locations and techniques. This substitution eliminates operator variability and ensures consistent, high-precision tissue acquisition across different procedures and operators.
3Productivity
If automated AI-based tissue acquisition planning is implemented, then procedure efficiency and accuracy improve, but the system complexity increases
Solution Approach 1:
The AI system performs multiple functions including anatomical image analysis, navigation path planning, tissue acquisition planning, and real-time procedure guidance. By consolidating these diverse functions into a single multi-functional platform, the system improves efficiency without proportionally increasing complexity.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between the operator and the complex task of tissue acquisition. The system processes complex image data and procedural decisions, presenting simplified recommendations to the operator, thereby improving efficiency while managing complexity through abstraction.
4Reliability
If deep anatomical regions are accessed with conventional endoscopes, then the target can be reached, but the risk of complications increases
Solution Approach 1:
The AI system performs preliminary analysis of anatomical structures before the procedure begins, identifying potential risks and planning safe navigation paths. By anticipating complications in advance and preparing mitigation strategies, the system reduces the risk of adverse events during deep anatomical access.
Solution Approach 2:
The patent replaces manual navigation with AI-based automated guidance that precisely controls the endoscope's path to deep anatomical regions. This substitution reduces human error and minimizes trauma to surrounding tissues, thereby lowering complication risks while maintaining reliable access to targets.
Data Source
AI summary
Systems, devices, and methods for planning an endoscopic tissue acquisition procedure for acquiring tissue from an anatomical target are disclosed. An endoscopic system comprises a steerable elongate instrument and a processor. The steerable elongate instrument can be positioned and navigated in a patient anatomy and acquire tissue from an anatomical target via a biopsy tool associated with the steerable elongate instrument. The processor can receive an image of the anatomical target, apply the received image to a trained machine-learning (ML) model to determine a tissue acquisition plan that includes a recommended biopsy tool and operational parameters for navigating the steerable elongate instrument or maneuvering the recommended biopsy tool. The tissue acquisition plan can be presented to a user, or used to facilitate a robot-assisted tissue acquisition procedure.