Pain is a severe and growing problem in the United States with more than 116 million Americans suffering from chronic pain and more than $635 billion is spent annually on pain and its related healthcare costs. Additionally, opioid addiction has become a national crisis with nearly 50,000 deaths every year as a result of opioid-involved overdoses and nearly $78.5 billion spent annually on opioid misuse and addiction. Currently available treatments for pain, namely opioid analgesics, have limited effectiveness and can lead to a significant number of side effects and complications including dependence, pharmacodynamic tolerance, sedation, gastrointestinal issues, respiratory depression, immunosuppression, and hormonal changes. Effectively treating pain requires an accurate assessment of pain, however current methods of diagnosing and evaluating pain depend on subjective self-reporting including the use of visual and numerical pain scales. The subjective nature of describing pain makes it virtually impossible to quantify and therefore difficult to treat and monitor. To overcome this subjectivity, through a non-invasive neuromodulation technique called transcranial direct current stimulation (tDCS) and deep learning, pain can be measured objectively using electroencephalograph (EEG) to assess and personalize treatment. The overarching goal of this project is to apply transcranial direct current stimulation (tDCS) as an alternative to opioids for the reduction in chronic pain. The investigator's long-term goal is to use these data to analyze EEG signals and generate personalized tDCS treatment in real time.
This clinical trial uses transcranial direct current stimulation (tDCS) using the patented tKIWI system to safely reduce self-reported chronic pain with little to no side effects to improve our understanding and ability to accurately diagnose pain disorders which would facilitate the development of pharmacologic and non-pharmacologic treatment modalities using deep learning architecture built into the tKIWI.