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GRASP Seminar: Shlomi Laufer, Technion – Israel Institute of Technology, “Enhancing Surgical Skill Assessment Through Computer Vision”
September 11 @ 2:00 pm - 3:00 pm
*This seminar was held in-person in Levine 307 as well as virtually…
ABSTRACT
Medical education traditionally employs the apprenticeship model, where trainees learn directly under the supervision of experienced practitioners. This model necessitates close follow-up and typically provides extremely subjective and non- standardized feedback. Over the years, efforts to introduce more objective assessment tools have gained momentum, although these tools often remain time-consuming and can still be influenced by subjective evaluations. Recently, the integration of motion sensors with medical simulators has provided a more objective form of feedback. However, they are typically limited to different aspects of motion economy. In this presentation, I will explore how advancements in computer vision can be utilized to create more informative assessments and feedback on surgical skills. I will also briefly discuss automatic assessment of anesthesiologists. Additionally, I will demonstrate how introducing cameras into the operating room provides a new avenue
for analyzing surgical workflows.
Presenter
Shlomi Laufer - Learn More
Shlomi Laufer is an Asst. Prof. at the Faculty of Data and Decision Sciences (formerly Industrial Engineering and Management) in the Technion – Israel Institute of Technology. He graduated (summa cum laude) from the Technion with a bachelor degree in Electrical Engineering (2004), Worked at Mobileye for a couple of years and then completed his PhD (2012) in Bio-Engineering at the Hebrew University working with Prof. Boris Rubinsky. He later had a joint appointment in Electrical Engineering and the Department of Surgery at the University of Wisconsin-Madison, working with Prof. Barry Van-Veen (EE) and Carla Pugh (Surgery). His research revolved around the use of sensor technology for the assessment of clinical skill. He joined the Technion in 2018, where he established the Sensor-analytics for ClinicAL Performance Lab (SCALPEL). His current work involves combining video and sensor data with computer vision and deep learning to automatically identify and assess clinical activities. He has installed camera systems in several operating rooms at Rambam Health Care Campus, where he analyzes the work of cardiac surgeons and anesthesiologists.