| Popis: |
The visual field (VF) examination is a useful clinical tool for monitoring a variety of ocular diseases. Despite its wide utility in eye clinics, the test as currently conducted is subject to an array of issues that interfere in obtaining accurate results. Visual field exams of patients suffering from additional ocular conditions are often unreliable due to interference between the comorbid diseases. To improve upon these shortcomings, virtual reality (VR) and deep learning are being explored as potential solutions. Virtual reality has been incorporated into novel visual field exams to provide a portable, 3D exam experience. Deep learning, a specialization of machine learning, has been used in conjunction with VR, such as in the iGlaucoma application, to limit subjective bias occurring from patients' eye movements. This chapter seeks to analyze and critique how VR and deep learning can augment the visual field experience by improving accuracy, reducing subjective bias, and ultimately, providing clinicians with a greater capacity to enhance patient outcomes. |
