RONov 3, 2021

Autonomous Magnetic Navigation Framework for Active Wireless Capsule Endoscopy Inspired by Conventional Colonoscopy Procedures

arXiv:2111.01977v120 citations
Originality Incremental advance
AI Analysis

This work addresses the need for improved diagnostic capabilities in gastrointestinal examinations, particularly for detecting suspicious lesions, but it is incremental as it builds on existing simultaneous magnetic actuation and localization methods.

The paper tackled the problem of inefficient and inaccurate navigation in active wireless capsule endoscopy by proposing an autonomous magnetic navigation framework that mimics conventional colonoscopy procedures, resulting in effective navigation in unknown, complex tubular environments with satisfactory accuracy, repeatability, and efficiency compared to manual operation.

In recent years, simultaneous magnetic actuation and localization (SMAL) for active wireless capsule endoscopy (WCE) has been intensively studied to improve the efficiency and accuracy of the examination. In this paper, we propose an autonomous magnetic navigation framework for active WCE that mimics the "insertion" and "withdrawal" procedures performed by an expert physician in conventional colonoscopy, thereby enabling efficient and accurate navigation of a robotic capsule endoscope in the intestine with minimal user effort. First, the capsule is automatically propelled through the unknown intestinal environment and generate a viable path to represent the environment. Then, the capsule is autonomously navigated towards any point selected on the intestinal trajectory to allow accurate and repeated inspections of suspicious lesions. Moreover, we implement the navigation framework on a robotic system incorporated with advanced SMAL algorithms, and validate it in the navigation in various tubular environments using phantoms and an ex-vivo pig colon. Our results demonstrate that the proposed autonomous navigation framework can effectively navigate the capsule in unknown, complex tubular environments with a satisfactory accuracy, repeatability and efficiency compared with manual operation.

Foundations

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