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Related Experiment Video

Updated: May 23, 2025

Diffuse Reflectance Spectroscopy: Getting the Capillary Refill Test Under One's Thumb
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Enhancing needle puncture detection using high-pass filtering and diffuse reflectance.

Rachael L'Orsa1,2,3,4, Anupam Bisht5, Linhui Yu2

  • 1Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany.

Frontiers in Robotics and AI
|May 21, 2025
PubMed
Summary
This summary is machine-generated.

Tension pneumothorax treatment via needle decompression has high failure rates. This study improved needle guidance using force and optical reflectance signals, achieving a 42.1% success rate, significantly enhancing safety and efficacy.

Keywords:
data-driven puncture detectionin-bore optical fibersneedle decompressionreflectancesignal processingtension pneumothorax

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Area of Science:

  • Biomedical Engineering
  • Medical Devices
  • Surgical Instrumentation

Background:

  • Tension pneumothorax is a life-threatening condition requiring rapid intervention.
  • Pre-hospital needle decompression has a high failure rate (up to 94.1%) and risks accidental tissue puncture.
  • Instrumented needles could improve accuracy in identifying the pleural cavity during needle decompression.

Purpose of the Study:

  • To investigate technical approaches for instrumented needle guidance in needle decompression.
  • To develop and evaluate a system for acquiring needle force, position, and optical reflectance signals.
  • To assess the effectiveness of data-driven puncture-detection algorithms with signal processing techniques.

Main Methods:

  • An experimental system was developed to collect needle force, position, and optical reflectance data.
  • Data were acquired using a percutaneous needle inserted into a simulated human chest anatomy (ex vivo porcine rib section).
  • Four data-driven puncture-detection (DDPD) algorithms were applied offline, with analysis of high-pass filters (HPFs) and multimodal methods.

Main Results:

  • Combining HPFs with DDPD on force signals improved maximum overall precision (MOP) by 2.7-fold (8.2% to 21.9%).
  • Applying HPF + DDPD to reflectance data achieved a peak MOP of 36.4%.
  • Multimodal data (reflectance + force) with HPF + DDPD yielded the highest MOP at 42.1%, a 5.1-fold improvement over force-only DDPD.

Conclusions:

  • High-pass filters significantly enhance data-driven puncture-detection algorithms for needle decompression.
  • Optical reflectance data, particularly when combined with force signals, offers superior performance for guiding needle placement.
  • The developed multimodal approach shows strong utility for improving the safety and efficacy of needle decompression procedures.