Computational Surgery: Helping Surgeons Avoid Mistakes with Better Robots

dc.contributor.author Kowalewski, Timothy M.
dc.contributor.corporatename Georgia Institute of Technology. Institute for Robotics and Intelligent Machines en_US
dc.contributor.corporatename University of Minnesota. Department of Mechanical Engineering en_US
dc.date.accessioned 2017-11-27T20:17:54Z
dc.date.available 2017-11-27T20:17:54Z
dc.date.issued 2017-11-08
dc.description Presented on November 8, 2017 from 12:15 p.m.-1:15 p.m. in the Scheller College of Business, Room 200, Georgia Tech. en_US
dc.description Timothy M. Kowalewski completed his Ph.D. in electrical engineering in quantitative surgical skill evaluation at the University of Washington’s Biorobotics Lab under the direction of Professor Blake Hannaford. His work was recognized with a best doctoral candidate award at the American College of Surgeons AEI Consortium on Surgical Robotics and Simulation. Kowalewski was a research scientist with DARPA’s “Traumapod: Operating Room of the Future” project. He commercialized his Ph.D. work for quantitative skill evaluation (CSATS Inc. and Simulab Corp., Seattle, Wash.) and also copioneered the use of crowdsourcing for high-volume assessment of surgical skills, a technique that enjoys increasingly widespread use in research and practice. Currently, Kowalewski is an assistant professor in the Department of Mechanical Engineering at the University of Minnesota, where he started the Medical Robotics and Devices Lab. en_US
dc.description Runtime: 57:40 minutes en_US
dc.description.abstract Preventable medical errors are the third leading cause of death in the United States. Despite over a decade of clinician-led efforts in policy and education, this issue remains. In the meantime, hospitals have adopted surgical robots at a dramatic pace. This provides opportunities to augment the art of surgery with more rigorous, quantitative science. This gives rise to the field of computational surgery which promises to address long-standing challenges in healthcare like the prevalence of human error. This talk will focus on two research problems in this area. First, how do we quantify and improve the existing skills of a surgeon? This requires a method whose scores correlate with patient outcomes, that can scale to cope with 51 million annual surgeries in the United States, and that can generalize across the diversity surgical procedures or specialties. Second, how can we build new robotic tools that render surgical tasks fundamentally easier, perhaps making errors impossible in the first place? This will survey multiple topics such as policy-blended human-robot shared control to ensure safety in robotic tissue grasping; novel patient-specific catheter robots that safely remove plaque via inverse design of soft robots and a theranostic excimer laser; and robotic 3D bioprinting directly onto moving human anatomy to explore new reconstructive procedures. en_US
dc.format.extent 57:40 minutes
dc.identifier.uri http://hdl.handle.net/1853/59001
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries IRIM Seminar Series
dc.subject Surgical robotics en_US
dc.subject Surgical skill evaluation en_US
dc.title Computational Surgery: Helping Surgeons Avoid Mistakes with Better Robots en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries IRIM Seminar Series
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isSeriesOfPublication 9bcc24f0-cb07-4df8-9acb-94b7b80c1e46
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