Yousi (Josey) is a third year MD student at Stanford University. She is a member of the Collaborative Haptics and Robotics in Medicine (CHARM) Lab under the direction of Dr. Allison Okamura, where her research focus lies in training for robotic surgery. More specifically, her interest lies in the use of virtual reality, haptics and error augmentation in surgical training paradigms.
Josey has a Bachelor of Science in Engineering (B.S.E.) degree with a major in Bioengineering and a minor in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania. She also completed a Master of Science in Robotics Engineering (M.S.E.) from the University of Pennsylvania under the direction of Dr. Katherine J. Kuchenbecker. She was a member of Dr. Kuchenbecker's Haptics Group in the General Robotics, Automation, Sensing, and Perception (GRASP) Lab, where she was interested in measurement and assessment of surgical skill in minimally invasive laparoscopic surgery using magnetic motion tracking and machine learning algorithms. In addition to her work in the GRASP Lab, Josey conducted research in a variety of other fields, including Pediatric Emergency Medicine, Cardiothoracic Surgery, and Radiological Imaging.
Education & Certifications
Master of Science (M.S.E.), University of Pennsylvania, Robotics Engineering (2016)
Bachelor of Science (B.S.E.), University of Pennsylvania, Bioengineering, Mechanical Engineering (2015)
Service, Volunteer and Community Work
Clinic Co-Coordinator, Cardiology Clinic (Cardinal Free Clinics) (3/2018 - 3/2019)
Menlo Park, CA
Robot-Assisted Surgical Training in a Complex Virtual Surgical Environment with Divergent and Convergent Force Fields
Teaching Assistant, Regional Study of Human Structure (SURG 101), Stanford University School of Medicine (1/2019 - 3/2019)
Clinical Development Engineering Intern, Intuitive Surgical (6/2018 - 12/2018)
Automatically rating trainee skill at a pediatric laparoscopic suturing task
SPRINGER. 2018: 1840–57
Minimally invasive surgeons must acquire complex technical skills while minimizing patient risk, a challenge that is magnified in pediatric surgery. Trainees need realistic practice with frequent detailed feedback, but human grading is tedious and subjective. We aim to validate a novel motion-tracking system and algorithms that automatically evaluate trainee performance of a pediatric laparoscopic suturing task.Subjects (n = 32) ranging from medical students to fellows performed two trials of intracorporeal suturing in a custom pediatric laparoscopic box trainer after watching a video of ideal performance. The motions of the tools and endoscope were recorded over time using a magnetic sensing system, and both tool grip angles were recorded using handle-mounted flex sensors. An expert rated the 63 trial videos on five domains from the Objective Structured Assessment of Technical Skill (OSATS), yielding summed scores from 5 to 20. Motion data from each trial were processed to calculate 280 features. We used regularized least squares regression to identify the most predictive features from different subsets of the motion data and then built six regression tree models that predict summed OSATS score. Model accuracy was evaluated via leave-one-subject-out cross-validation.The model that used all sensor data streams performed best, achieving 71% accuracy at predicting summed scores within 2 points, 89% accuracy within 4, and a correlation of 0.85 with human ratings. 59% of the rounded average OSATS score predictions were perfect, and 100% were within 1 point. This model employed 87 features, including none based on completion time, 77 from tool tip motion, 3 from tool tip visibility, and 7 from grip angle.Our novel hardware and software automatically rated previously unseen trials with summed OSATS scores that closely match human expert ratings. Such a system facilitates more feedback-intensive surgical training and may yield insights into the fundamental components of surgical skill.
View details for DOI 10.1007/s00464-017-5873-6
View details for Web of Science ID 000427159300027
View details for PubMedID 29071419
View details for PubMedCentralID PMC5845064
Cecal ligation and puncture accelerates development of ventilator-induced lung injury
AMERICAN JOURNAL OF PHYSIOLOGY-LUNG CELLULAR AND MOLECULAR PHYSIOLOGY
2015; 308 (5): L443–L451
Sepsis is a leading cause of respiratory failure requiring mechanical ventilation, but the interaction between sepsis and ventilation is unclear. While prior studies demonstrated a priming role with endotoxin, actual septic animal models have yielded conflicting results regarding the role of preceding sepsis on development of subsequent ventilator-induced lung injury (VILI). Using a rat cecal ligation and puncture (CLP) model of sepsis and subsequent injurious ventilation, we sought to determine if sepsis affects development of VILI. Adult male Sprague-Dawley rats were subject to CLP or sham operation and, after 12 h, underwent injurious mechanical ventilation (tidal volume 30 ml/kg, positive end-expiratory pressure 0 cmH2O) for either 0, 60, or 120 min. Biochemical and physiological measurements, as well as computed tomography, were used to assess injury at 0, 60, and 120 min of ventilation. Before ventilation, CLP rats had higher levels of alveolar neutrophils and interleukin-1β. After 60 min of ventilation, CLP rats had worse injury as evidenced by increased alveolar inflammation, permeability, respiratory static compliance, edema, oxygenation, and computed tomography. By 120 min, CLP and sham rats had comparable levels of lung injury as assessed by many, but not all, of these metrics. CLP rats had an accelerated and worse loss of end-expiratory lung volume relative to sham, and consistently higher levels of alveolar interleukin-1β. Loss of aeration and progression of edema was more pronounced in dependent lung regions. We conclude that CLP initiated pulmonary inflammation in rats, and accelerated the development of subsequent VILI.
View details for DOI 10.1152/ajplung.00312.2014
View details for Web of Science ID 000351062000004
View details for PubMedID 25550313
View details for PubMedCentralID PMC4346777