Dynamic Arthroscopic Navigation System for Anterior Cruciate Ligament Reconstruction Based on Multi-level Memory Architecture

2025-04-28

AI Summary

Conventional static arthroscopic navigation for anterior cruciate ligament (ACL) reconstruction suffers from insufficient robustness under dynamic intraoperative conditions—including viewpoint changes, instrument occlusion, and soft-tissue deformation. Method: This work proposes the first markerless dynamic video navigation method for ACL reconstruction, built upon a novel multi-level memory architecture inspired by the Atkinson-Shiffrin three-stage memory model (sensory, working, and long-term memory). The lightweight, hardware-agnostic framework integrates deep feature matching, motion consistency constraints, and real-time pose estimation to achieve continuous, stable tracking of the femoral condyle in dynamic arthroscopic video. Results: The system operates at 25.3 FPS with an end-to-end latency of 39.5 ms. On 1000-frame sequences, it achieves a localization error of 5.3±1.5 pixels—45% lower than static methods—with accuracy improvements of 35% and 19% on medium- and short-length sequences, respectively.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

This paper presents a dynamic arthroscopic navigation system based on multi-level memory architecture for anterior cruciate ligament (ACL) reconstruction surgery. The system extends our previously proposed markerless navigation method from static image matching to dynamic video sequence tracking. By integrating the Atkinson-Shiffrin memory model's three-level architecture (sensory memory, working memory, and long-term memory), our system maintains continuous tracking of the femoral condyle throughout the surgical procedure, providing stable navigation support even in complex situations involving viewpoint changes, instrument occlusion, and tissue deformation. Unlike existing methods, our system operates in real-time on standard arthroscopic equipment without requiring additional tracking hardware, achieving 25.3 FPS with a latency of only 39.5 ms, representing a 3.5-fold improvement over our previous static system. For extended sequences (1000 frames), the dynamic system maintained an error of 5.3 plus-minus 1.5 pixels, compared to the static system's 12.6 plus-minus 3.7 pixels - an improvement of approximately 45 percent. For medium-length sequences (500 frames) and short sequences (100 frames), the system achieved approximately 35 percent and 19 percent accuracy improvements, respectively. Experimental results demonstrate the system overcomes limitations of traditional static matching methods, providing new technical support for improving surgical precision in ACL reconstruction.

Problem

• Enables dynamic tracking in ACL reconstruction surgery
• Improves accuracy without additional hardware requirements
• Addresses challenges like occlusion and tissue deformation

Innovation

• Dynamic video tracking replaces static image matching
• Multi-level memory architecture ensures continuous navigation
• Real-time performance on standard equipment without extra hardware