Performing the HINTS-exam using a mixed-reality head-mounted display in patients with acute vestibular syndrome: a feasibility study - Nadia Sadok; Gijs Luijten; Gijs Luijten; Gijs Luijten; Fin H. Bahnsen; Christina Gsaxner; Lorenz Peters; Theda Eichler; Theresa Rombach; Stephan Lang; Sameh Khattab; Jens Kleesiek; et al. (Frontiers in Neurology)
BackgroundIn patients with acute vestibular syndrome (AVS) differentiating between benign acute peripheral vestibular disorders and possible life-threatening central, causes such as stroke, can be challenging due to similar symptoms. AVS patients experience dizziness, vertigo, imbalance, nausea, vomiting, and abnormal eye movements. This research evaluates the feasibility of using the eye-tracking capability of a mixed reality optical-see-through head-mounted display (MR-OST-HMD) to detect pathological eye movement patterns in patients with AVS.MethodsConducted at University Hospital Essen, this study assessed patients with AVS using a MR-OST-HMD during the HINTS-Exam. The feasibility study included 21 healthy subjects, seven patients with acute peripheral vestibular dysfunction and two stroke patients. Eye gaze, head position, and orientation were captured using a MR-OST-HMD and an in-house developed application designed to simulate the HINTS-Exam. The eye-tracking technology determined gaze direction and position, while the internal measurement unit and gyroscope recorded head movements in terms of position and velocity.ResultsThe MR-OST-HMD detected abnormal eye movements, including nystagmus, saccades, and skew deviation effectively. The device proved effective even for patients with severe nausea and elderly participants, who completed the eye calibration and HINTS-Exam without difficulty. The MR-OST-HMD HINTS-Exam was quick to perform (approximately 5 min) and was easily integrated into clinical practice after a single demonstration for medical staff.ConclusionMR-OST-HMD can detect pathological eye movements in AVS patients. Future research should validate these findings in larger cohorts and explore machine learning integration to enhance diagnostic accuracy.