AI-Powered Implant Offers Hope for Chronic Pain Sufferers

A new study details an innovative approach to chronic pain management: an AI-driven wireless device that interprets brain activity to personalize spinal cord stimulation, offering a potential alternative to addictive medications.

Breakthrough Device Decodes and Treats Pain

The collaborative research, led by Laiming Jiang, Jun Chen, and Qifa Zhou, showcases a system that includes a flexible, implantable stimulator, a wearable ultrasound transmitter, and an AI algorithm. The device aims to decode brain activity related to pain levels.

Here we report an integrated flexible ultrasound-induced wireless implantable stimulator combined with a pain detection and management system for personalized chronic pain management, the co-authors reported.

The Global Impact of Chronic Pain

Chronic pain, defined as pain lasting over three months, affects a significant portion of the global population. The CDC estimates that over 20% of adults in the United States had chronic pain in 2019 (CDC).

It is estimated one in five people globally experience chronic pain, and in the U.S., about 50 million adults struggle with chronic pain daily. Anxiety and depression frequently accompany chronic pain, impacting sufferers’ overall well-being.

The Urgent Need for Non-Opioid Solutions

Researchers note that Chronic pain management typically involves opioids, which are associated with severe side effects such as addiction. The reliance on opioids has fueled a global addiction crisis, underscoring the need for alternative treatments.

Given the severe risks of opioid addiction, there is a growing demand for personalized, drug-free approaches to manage chronic pain effectively.

AI-Driven Personalized Treatment

The research team’s solution uses AI to classify pain levels as slight, moderate, or extreme by monitoring electroencephalogram (EEG) brain activity. The AI then adjusts the ultrasound transmitter’s acoustic energy, tailoring the electrical stimulation to the patient’s specific pain intensity.

The AI model, based on ResNet-18, demonstrated a high accuracy rate of 94.8 percent in classifying pain levels in lab rodents, according to a report from the USC Viterbi School of Engineering.

We classify pain stimuli from brain recordings by developing a machine learning model and program the acoustic energy from the ultrasound transmitter and therefore the intensity of electrical stimulation, the researchers explained.

We show that the implant can generate targeted, self-adaptive, and quantitative electrical stimulation to the spinal cord, according to the classified pain levels for chronic pain management in free-moving animal models, the scientists concluded.

This pioneering work offers a promising path toward personalized, non-pharmaceutical chronic pain management using AI and electrical stimulation.