- A $996,413 Phase II SBIR grant from the NSF advances ReSuture’s efforts to quantify operative performance through simulated surgical platforms, capacitive sensing, and machine learning
- ReSuture’s proposed technology – a combination of lifelike physical simulations, novel sensors, and robust data analysis – is addressing a universal market need that has been unsolved for decades
- ReSuture’s early working solutions have been adopted by device manufacturers W.L. Gore, Teleflex Medical, Becton Dickinson, Endologix, and surgical residency programs at Tulane Medical Center, McMaster University, and the Mayo Clinic
ReSuture, Inc, (ReSuture) a dynamic early-stage business developing synthetic training models to expedite the adoption of life-saving therapies and medical devices, today announced it has received a $996,413 Small Business Innovation Research (SBIR) grant from the National Science Foundation (NSF). The grant will support the commercialization of ReSuture’s products and technology to evaluate vascular surgical operative performance in both open and endovascular fields. The grant, entitled “SBIR Phase II: Quantification of Operative performance through Simulated Surgical Platforms, Capacitive Sensing, and Machine Learning to Improve Surgeon Performance and Medical Device Development,” will be conducted in conjunction with Arizona State University (ASU). Collaborating with Dr. Baoxin Li and his lab at the ASU Department of Computer Science & Engineering, ReSuture will develop and employ machine learning algorithms to effectively classify surgical skills using data collected from ReSuture’s sensing technology.
