Miniature Multi-modal Medical Robots

Bio: Yash Chitalia is an Assistant Professor in Mechanical Engineering, Assoc. in Neurosurgery and Radiation Oncology, and the director of the Healthcare Robotics and Telesurgery (HeaRT) Laboratory at the University of Louisville. Prior to his faculty position, he worked as a Research Fellow at the Boston Children’s Hospital and Harvard Medical School. He received his Ph.D. in Mechanical Engineering from the Georgia Institute of Technology, an MS in Electrical Engineering from the University of Michigan at Ann Arbor, and a BE in Electrical Engineering from the University of Mumbai, India. His research vision is to revolutionize the field of minimally invasive surgery by designing semi-autonomous micro-and meso-scale surgical robots. Surgical success today depends heavily on our ability to detect, reach, and remove any cause of morbidity. In each of these sub-requirements, surgeons would benefit from dexterous intelligent robotic tools. Yash’s research aims to design such tools using micro-scale machining, 3D-printing, modeling of super-elastic structures and joints to control dexterous continuum robots. He collaborates with interventional radiologists and neurosurgeons from the University of Louisville Neurosurgery, Brown Cancer Center, Boston Children’s Hospital, Emory University Hospital, and Children’s Healthcare of Atlanta (CHOA) to design his robots.
Talk Abstract: Manual manipulation of traditional minimally invasive devices for life-saving surgery is time consuming with uncertain results. Steerable robotic micro-catheters and miniature endoscopes are essential to the operating room of the future, but lack the dexterity needed to navigate tortuous human anatomy. This talk introduces the design of three unique miniature robots: 1) A robotic catheter to deliver targeted electrical stimulation to the spinal cord, 2) a miniature robotically steered stylet for deep-seated cancers, and 3) a laser-steering robot for minimally invasive neurological surgery. These robots demonstrate follow-the-leader motion (i.e. the ability to snake through tortuous anatomy) due to their use of multi-modal actuation strategies. A mathematical model is discussed that allows us to predict the shape of these multi-modal robots. This model allows us to accurately predict the “bending and twisting” behavior seen in these type of robots, which is critical for their safe operation in surgical scenarios.