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BPN765: Full-Field Strain Sensor for Hernia Mesh Repairs

Project ID BPN765
Website
Start Date Tue 2014-Aug-12 22:09:06
Last Updated Thu 2014-Aug-14 15:23:03
Abstract Each year, more than 400,000 ventral hernia repairs are performed in the United States. A hernia is the protrusion of an organ through a weak spot in the surrounding muscle or connective tissue that normal contains it. Large ventral hernias (hernias that occur in the abdominal wall) are typically treated by suturing in a surgical mesh to cover and overlap the hernia defect. The surgical mesh provides additional support to the damaged tissue surrounding the hernia. However, in 25-40% of patients, the hernia repair fails, resulting in recurrence of the hernia, along with other complications including infection and intestinal obstruction. We hypothesize that a major cause of hernia recurrence is the unequal distribution of stress across the mesh resulting in high stress concentrations at the tissue-mesh interface, particularly at the site of mesh fixation to the abdominal wall muscles. Over time the mesh is pulled away from the abdominal wall at the high stress concentrations and the hernia defect recurs. We propose to design a biocompatible, instrumented patch, capable of mapping the 2D strain topography placed on the mesh. The sensor will enable surgeons to actively identify and address areas of high stress during the surgery by modifying the surgical procedure to redistribute stress more evenly, thus decreasing the rate of hernia recurrence. Furthermore, our long term goal is to design a hernia mesh that contains strain gauges and the associated circuitry such that once implanted in the body the prosthetic can alert patients when they are engaging in activities that place high stress on the implant. Such a dynamic, interactive hernia mesh would empower patients to actively participate in their post- operative care in a way that is personalized and unprecedented in surgery.
Status New
Funding Source NSF
IAB Research Area Physical Sensors & Devices
Researcher(s) Amy Liao
Advisor(s) Michel M. Maharbiz
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