Aaron Kotranza, Ben Lok, Juan Cendan University of Florida Colleges of Medicine and Engineering

1. Aaron Kotranza, Ben Lok, Juan Cendan University of Florida Colleges of Medicine and Engineering Gainesville, FL MedBiquitous Annual Conference, London, 2010 Presentation 39558 Simulation of a Virtual Patient with Cranial Nerve Injury Augments Physician-Learner Concern for Patient Safety

2. NERVE • Neurological Exam Rehearsal Virtual Environment (NERVE) • Virtual Patient targets abnormal physical exam findings of the cranial nerves • Interaction includes: • Verbal communication with natural speech • Gestures • Virtual tool interactions • Patient Vision Feedback (PVF) allows the user to share the patient visual experience

3. Motivation • Cranial nerve palsy/injury is uncommon but dramatic and critical physical finding • Cannot be feigned by SP • Opportunities for clinical teaching are rare • Identified as a particularly relevant niche for virtual human representation

4. Current State of the Art • Traditional lecture, textbook-video didactics, some simulation systems • Fundoscopic exams • UC Davis disembodied eyes system • http://cim.ucdavis.edu/eyes/version15/eyesim.html • Rick Lasslo,M.D., M.S., Gary Henderson, PhD, and John Keltner, M.D. , UC Davis School of Medicine

5. System Description • NERVE uses a life-size virtual patient with cranial nerve injury • Learner performs examination using • Speech • Virtual tools (ophthalmoscope, eye chart, hand and fingers) manipulated by a Nintendo Wii Remote (WiiMote) • Representation from the VP • Restricted eye motion • Double vision • Relevant history

6. NERVE System • VP modeled in Autodesk Maya • Rendered with open source Ogre 3D • VP portrayed on a 52” LCD • User wears IR fiducials for location/angle of vision tracking • Naturalpoint Optitrack

7. Patient Vision Feedback • Real-time experience • Allows learner to view a virtual room through the eyes of the patient • Goals • Provide learner with more information for understanding how a particular CN injury affects the patient’s vision • Provide insight into the patient’s overall wellbeing and safety

8. Typical Interaction • Learner greets VP and queries with natural speech • Virtual People Factory • Learner performs examination • Ophthalmoscope with and without light • Fundoscopic exam • Hand fingers tool for ROM • Convergence • Peripheral Vision • Eye chart with “cover one eye”

9. Interaction with VP using text box • Typical exchange between user and virtual patient - can occur in natural spoken language or in text-box

10. Additional Responses • Also able to reproduce on command • Head tilt • Chin to chest • Smile, frown • Stick out tongue • Raise eyebrows and wink • Puff out cheeks • Turn head side-to-side • Collision detection allows examination of Cranial Nerve 5 (virtual finger and facial sensation)

11. Special Challenge • Eye movement requires coordination of 6 muscles • Characteristic presentation when abnormal would require tremendous computational expense • Developed our own model that is not physically based, but consistent with physiologic presentation

12. Special Challenge • Visual model uses interpolation of 8 cardinal eye movements (left, up-left,…, down) • Can be visualized as a 2-D pitch-yaw plane • For each eye the model defines a set of 8 vector criteria

13. Range of Motion • Typical Range Examination • Red/Green boxes as teaching tools

14. Wii Remote • Shaped like a hand-held tool and high degree of freedom control • Bluetooth, 11 buttons, 3 orthogonal accelerometers, 45 degree field-of-view IR camera that tracks up to 4 points • Displays information with integrated LEDs, speaker and vibration • 100Hz update and sub-centimeter accuracy • Inexpensive

15. WiiMote Applications • Ophthalmoscope • Example: “Contact” with eye leads to vibration and complaint from patient • Trigger button used to create light for pupil examination

16. Pupil Reflex Example • Exam of the pupil using laptop • Can be done with WiiMote

17. Patient Vision Feedback System • Head mounted display • Virtual room is rendered twice reflecting the perspective of each eye

18. Methods • 18 students in second year of medical school • Had prior experience with “normal” neurological exam • Had not examined a patient with active pathology • 9 students experienced PVF prior to examining the patient • The other 9 did so after data collection

19. Methods 2 – Examination Experience “PV” Passive – Sees what the patient sees… “NPV” Active – Interviews and examines patient • Two participants at a time • Completed experience survey • NERVE and PVF were explained by instructor (Author AK) • Participant PV donned a stereoscopic HMD displaying the virtual room while participant NPV examined the NERVE • Both participants are synchronized to the same patient exam and environment

20. Methods 3 -Counterbalanced Design Both users complete survey, switch roles Both users complete final survey

21. Results • 2 participants never experienced double vision • One in each group • ? Technical, individual …unclear and removed from analysis • Correct diagnosis was established equally by both groups • 9 of 9 in PV; 8 of 9 in NPV (p=NS)

22. Results 2 • PVF provided enough data to accurately diagnose the CN injury • 81% were able to make the Dx using PVF only • 7/8 in PV group identified CN3 palsy • 6/8 in NPV group identified CN6 palsy • 2 = 5.1; p<0.05

23. Results 3 • Concern for Patient Safety • Affective performance (perspective taking leading to concern for patient wellbeing) evaluated as expressed (verbal or written) concern for safety • E.g. Informing the VP that he should not drive home, etc. • 7/8 learners in PV expressed concern vs. 0/8 in the NPV group (p<0.005 Fisher’s exact) • 5 on post patient vision survey • 1 of five told the patient directly during the exchange • 3 on the overall survey • 1 expressed concern in both surveys

24. Results 4 • Note • All participants were primed to think about driving as the opening statement from the VP was • “I was driving home from work and all of the sudden the lines on the road started to cross.”

25. Conclusions • NERVE presents a life-sized virtual human presenting with a cranial nerve palsy • Novel vector methodology provides efficient modeling • Content validity has been completed on prior testing with 32 students and residents • Students can arrive at the correct diagnosis using traditional examination skills as well as “through the eyes of the patient” technique • Patient safety will be augmented by giving the examiner a patient-perspective of the presenting condition

26. Future and Support • Currently developing and testing a web-deployable version of NERVE • Will not initially support voice recognition but can type-in-box • Will not support patient vision • Widely deployable • Dr. Cendan and Dr. Lok receive support from NIH NLM R03-LM009646.

27. Early Web-Based User Data, n=14