USC engineers have developed a flexible spinal device powered by ultrasound that adapts to pain levels in real time, potentially reducing opioid dependence in 51.6 million Americans.
Wireless implants that use spine to bend and understand a single pain pattern can provide an addictive opioid alternative to millions of chronic pain patients. Researchers at the University of Southern California have created a device that monitors brain signals, uses artificial intelligence to assess pain severity, and provides precisely calibrated electrical stimulation—all without batteries or invasive wiring.
Ultrasound-driven implants represent a significant advancement in current spinal cord stimulators that require bulky batteries and frequent surgical replacements. The device, published in Natural Electronics, achieved 94.8% accuracy in distinguishing mild, moderate and extreme pain levels in animal studies.
Smart pain without risk of surgery
Current spinal cord stimulators help patients avoid opioids by blocking pain signals in the brain, but they have serious drawbacks. These devices cost tens of thousands of dollars, require invasive surgery to place the battery, and require a replacement procedure as the battery wears out.
“What really sets the device apart is its wireless, intelligent and adaptive capabilities,” explained Qifa Zhou, who leads the research team. “We believe it has great potential to replace alternative pharmacological regimens and conventional electrical stimulation methods.”
New implants resolve these issues through wireless power delivery. The wearable ultrasonic transmitter transfers energy through the skin to the piezoelectric receiver, converting sound waves into electricity. The flexible design allows the device to move naturally on the spine without breaking the connection.
AI real-time reading pain
The most innovative feature of the system is its ability to automatically adjust treatment. This is how the closed-loop process works:
- Brain monitoring sensors detect electroencephalography (EEG) signals that reflect pain intensity
- Machine learning algorithms analyze these signals and divide pain into three levels
- External ultrasound transmitters automatically adjust energy output based on the detected pain severity
- The implant converts this energy into electrical stimulation targeting specific pain levels
In laboratory tests, the system successfully treated mechanical pain (such as acupuncture) and thermal pain (from infrared heat) in animal models. The rats showed a clear preference for the environment in which the pain management system was activated, confirming the effectiveness of the device.
Beyond current solutions
Principal investigator Yushun Zeng highlighted the clinical advantages: “By utilizing wireless ultrasound energy transfer and closed-loop feedback systems, this UIWI stimulator eliminates the need for bulky implanted batteries and can be adjusted in real time, with accurate and painful regulation.”
The device addresses key limitations of existing treatments. Fixed-rate stimulation often proves to be a deficit of severe pain attacks, and the new system automatically expands its response. When researchers tested different pain intensities, adaptive stimulation always outperformed the fixed-rate approach.
The implant uses lead zirconium titanate (PZT), a highly efficient material that converts ultrasound into electrical energy. The team found that their composite design reached an energy efficiency of 3.57%, which is sufficient for therapeutic stimulation while maintaining within the FDA safety limits for ultrasound exposure.
Future applications
Looking ahead, Zhou also envisions smaller devices that can be injected with a simple syringe. An external ultrasound transmitter may evolve into an unbound patch that combines imaging capabilities with energy transfer, possibly controlled through a smartphone application.
The study involved large-scale medical challenges. Chronic pain affects more than 50 million Americans, and 17 million people experience severe pain, severely limiting daily activities. Current opioid-based treatments have significant addiction risks and side effects.
Although the technology requires further development before human trials, successful animal studies have shown that wireless, adaptive pain management can alter the treatment of chronic pain conditions. This approach represents a shift to personalized medicine, which responds to individual pain patterns rather than providing a unified treatment.
Chen Zhen, co-leader, points to the clinical implications: “Incorporating deep learning-based pain assessments can dynamically explain and respond to fluctuating pain states, which is crucial for adapting to patient-specific variability.”
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