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Combining Fiber Bragg Grating and Artificial Intelligence Technologies for Supporting Epidural Procedures.

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    This study introduces an AI-driven system using Fiber Bragg grating sensors to improve loss of resistance (LOR) detection during epidural procedures. This novel approach enhances accuracy and reduces failure rates in clinical settings.

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    Area of Science:

    • Biomedical Engineering
    • Medical Technology
    • Artificial Intelligence

    Background:

    • Loss of resistance (LOR) is a standard technique for epidural punctures, but it has a high failure rate.
    • Existing solutions for LOR detection include Fiber Bragg grating (FBG) sensors and artificial intelligence (AI).

    Purpose of the Study:

    • To develop and evaluate an AI-driven system for enhanced Loss of Resistance (LOR) detection during epidural procedures.
    • To integrate FBG sensor technology with AI for real-time LOR identification.

    Main Methods:

    • Developed a custom FBG sensor and an AI-based system for automatic labeling and identification of LOR events.
    • Tested the system using data from 10 patients undergoing epidural procedures for chronic back pain.
    • Employed a Support Vector Machine with a Leave-One-Out strategy for LOR event identification.

    Main Results:

    • The automatic labeling retrospectively identified all LOR events.
    • The AI-based identification demonstrated high accuracy in detecting LOR events.
    • The system achieved a minimal rate of false positives in real-time emulation.

    Conclusions:

    • The combined AI and FBG approach shows promising performance for automatic LOR detection.
    • This technology integration can significantly improve clinical support during epidural punctures.
    • The findings suggest a potential to reduce unsuccessful epidural procedures and advance pain management.