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Analgesia and Pain Management

Analgesia and Pain Management

Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...

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Summary

This study introduces a novel method for pain estimation using fractional-order impedance models and deep learning. This approach offers objective, personalized pain prediction to improve patient safety and avoid medication over-dosing.

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Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Pain Management

Background:

Reliable and objective pain measurement remains a significant challenge in clinical practice.
Current pain assessment methods, like the Numeric Rating Scale (NRS), are subjective and prone to variability.
Post-surgical pain management requires accurate monitoring to prevent under- or over-dosing of analgesics.

Purpose of the Study:

To develop and validate a more reliable method for post-surgical pain estimation.
To enhance pain assessment objectivity by integrating fractional-order impedance models (FOIM) and deep learning (convolutional neural networks - CNNs).
To explore the potential of AI in personalized nociception-antinociception prediction for closed-loop analgesia systems.

Main Methods:

Utilized a recursive identification method to parameterize fractional-order impedance models (FOIM) from skin impedance measurements.
Employed deep learning, specifically convolutional neural networks (CNNs), for pain classification using time-frequency data and spectrograms.
Validated online parameter estimates and CNN predictions against patient-reported pain levels (NRS) in a proof-of-concept clinical trial.

Main Results:

Changes in FOIM parameters and online-retrained CNN predictions correlated with nociception trends and NRS scores.
The developed models demonstrated the ability to predict pain intensity and tissue heterogeneity.
Online predictions from retrained CNNs showed more specific trends compared to offline population-trained models.

Conclusions:

Tailored online identification and deep learning methods can provide objective pain estimations even in artifact-corrupted environments.
AI-driven models offer personalized nociception-antinociception prediction, crucial for patient safety.
These advancements support the design and evaluation of closed-loop analgesia controllers for improved pain management.