USC engineers develop flexible spinal device powered by ultrasound that adapts to pain levels in real-time, potentially reducing opioid dependence for 51.6 million Americans.
A thumb-sized wireless implant that bends with the spine and learns individual pain patterns could offer millions of chronic pain sufferers an alternative to addictive opioids. Researchers at USC have created a device that monitors brain signals, assesses pain severity using artificial intelligence, and delivers precisely calibrated electrical stimulation—all without batteries or invasive wiring.
The ultrasound-powered implant represents a significant advance over current spinal cord stimulators, which require bulky batteries and frequent surgical replacements. Published in Nature Electronics, the device achieved 94.8% accuracy in distinguishing between slight, moderate, and extreme pain levels in animal studies.
Smart Pain Relief Without Surgery Risks
Current spinal cord stimulators help patients avoid opioids by blocking pain signals to the brain, but they come with serious drawbacks. The devices cost tens of thousands of dollars, require invasive surgery for battery placement, and need replacement procedures every few years as batteries wear out.
“What truly sets this device apart is its wireless, smart and self-adaptive capability for pain management,” explained Qifa Zhou, who led the research team. “We believe it offers great potential to replace pharmacological schemes and conventional electrical stimulation approaches.”
The new implant solves these problems through wireless power delivery. A wearable ultrasound transmitter sends energy through the skin to a piezoelectric receiver that converts sound waves into electricity. The flexible design allows the device to move naturally with the spine without breaking connections.
AI Reads Pain in Real-Time
The system’s most innovative feature is its ability to automatically adjust treatment. Here’s how the closed-loop process works:
- Brain monitoring sensors detect electroencephalogram (EEG) signals that reflect pain intensity
- A machine learning algorithm analyzes these signals and classifies pain into three levels
- The external ultrasound transmitter automatically adjusts energy output based on detected pain severity
- The implant converts this energy into electrical stimulation tailored to the specific pain level
In laboratory tests, the system successfully treated both mechanical pain (like pinpricks) and thermal pain (from infrared heat) in animal models. Rats showed clear preferences for environments where the pain management system was activated, confirming the device’s effectiveness.
Beyond Current Solutions
Lead researcher Yushun Zeng emphasized the clinical advantages: “By leveraging wireless ultrasonic energy transfer and closed-loop feedback system, this UIWI stimulator removes the necessity for bulky implanted batteries and allows for real-time, precisely adjustable pain modulation.”
The device addresses a critical limitation of existing treatments. Fixed-rate stimulation often proves inadequate for severe pain episodes, while the new system automatically scales its response. When researchers tested different pain intensities, adaptive stimulation consistently outperformed fixed-rate approaches.
The implant’s core uses lead zirconate titanate (PZT), a highly efficient material for converting ultrasound into electrical energy. The team found that their composite design achieved 3.57% energy efficiency—sufficient for therapeutic stimulation while staying within FDA safety limits for ultrasound exposure.
Future Applications
Looking ahead, Zhou envisions even smaller devices that could be implanted with a simple syringe injection. The external ultrasound transmitter might evolve into an untethered patch that combines imaging capabilities with energy delivery, potentially controlled through smartphone apps.
The research addresses a massive healthcare challenge. Chronic pain affects over 50 million Americans, with 17 million experiencing high-impact pain that severely limits daily activities. Current opioid-based treatments carry significant addiction risks and side effects.
While the technology requires further development before human trials, the successful animal studies demonstrate that wireless, adaptive pain management could transform treatment for chronic pain conditions. The approach represents a shift toward personalized medicine that responds to individual pain patterns rather than delivering uniform treatment.
Chen Gong, a co-lead author, noted the clinical significance: “Incorporating deep learning–based pain assessment enables dynamic interpretation and response to fluctuating pain states, which is essential for accommodating patient-specific variability.”
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