Flexible AI-Powered Smart Bandage for Detecting and Treating Infection
Abstract
Over 45,000 United States Warriors have been severely wounded in the Iraq and Afghanistan wars. Historically, wound infections represent a major complication of combat-related injuries. Studies from Iraq and Afghanistan have indicated that wound infections occur at rates up to 25% with substantial morbidity and mortality. Those who do develop infection are at risk for sepsis or multisystem organ failure and death. Current standard-of-care dressings are passive and do not actively respond to changes in the wound. As a result, patients require frequent monitoring, adding to treatment cost and stress on limited medical resources in forward deployment areas. For civilian patients, wound infections can present insidiously and result in delayed wound healing, reduced quality of life and add to health care costs. Early diagnosis of infection is important to allow for timely initiation of treatment and to prevent worsening infection and related complications once hemostasis has occurred. Currently, wound infection remains a clinical diagnosis based on cardinal symptoms of increased pain, redness, swelling and warmth. Unfortunately, early diagnosis is not always feasible in the battlefield setting due to logistical and resource limitations. Therefore, any new technology that can identify and/or treat infection early and promptly has the potential to revolutionize battlefield wound care and improve outcomes for patients. Smart bandages represent an emerging field in translational research and are well positioned to address challenges in early diagnosis by integrating specialized biosensors for real-time wound monitoring and active treatment. Our proposed study is strongly in alignment with the FY22 DMRDP-BWMIR focus areas of combat wound physiology and support tools for combat wound care. We will develop a solution for both warfighters and civilians with soft tissue injuries resulting in open wounds requiring wound care. Our proposed solution is a flexible AI-powered smart bandage that can both detect and treat early wound infection once hemostasis has been achieved. To achieve this goal, we will leverage our collaboration with the Zhenan Bao group at Stanford, whose research has made important advancements in the field of wearable bioelectronics. The AI- powered smart bandage will contain sensors that can detect changes in wound temperature and/or impedance that occur during bacterial colonization and early infection. Upon sensing a new infection, the device acts through a closed-loop feedback system to deliver electrical stimulation to the wound. Electrical stimulation has been shown to reduce bacterial colonization, improve blood flow, and promote immune function, thus helping to return the wound to a non-infected state to promote healing. In addition to providing precise electrical stimulation, the dressing will be highly conforming, provide physical coverage over the wound and help provide a moist healing environment. By delivering electrical stimulation only in response to infection, the dressing can be battery powered to last for at least 5-7 days, meeting the needs of prolonged care in forward deployment situations. This smart bandage will be light, comfortable, and easy-to-use in the field by combat medics in any operational setting. Application of the dressing requires minimal infrastructure or training, and anyone could potentially be trained to place or change the dressing as a replacement to standard wound dressings. Use of the AI-powered smart bandage would be as simple as opening the package and placing the bandage on top of the wound. Given the ease of use of this dressing, it would be an ideal solution for non-combat personnel and civilians as well. The device would be financially viable as electrical stimulation is already reimbursed by CMS and other third-party payers. Given our existing collaboration with the Bao lab, we have already developed prototypes for similar devices and a
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2024
- Source ID
- HT94252311066
Entities
People
- Geoffrey Gurtner
Organizations
- United States Army
- University of Arizona