Performance of complex visual tasks using simulated prosthetic vision via augmented-reality glasses

Abstract

Photovoltaic subretinal prosthesis is designed for restoration of central vision in patients with age-related macular degeneration (AMD). We investigated the utility of prosthetic central vision for complex visual tasks using augmented-reality (AR) glasses simulating reduced acuity, contrast, and visual field. AR glasses with blocked central 20° of visual field included an integrated video camera and software which adjusts the image quality according to three user-defined parameters: resolution, corresponding to the equivalent pixel size of an implant; field of view, corresponding to the implant size; and number of grayscale levels. The real-time processed video was streamed on a screen in front of the right eye. Nineteen healthy participants were recruited to complete visual tasks including vision charts, sentence reading, and face recognition. With vision charts, letter acuity exceeded the pixel-sampling limit by 0.2 logMAR. Reading speed decreased with increasing pixel size and with reduced field of view (7°-12°). In the face recognition task (four-way forced choice, 5° angular size) participants identified faces at >75% accuracy, even with 100 μm pixels and only two grayscale levels. With 60 μm pixels and eight grayscale levels, the accuracy exceeded 97%. Subjects with simulated prosthetic vision performed slightly better than the sampling limit on the letter acuity tasks, and were highly accurate at recognizing faces, even with 100 μm/pixel resolution. These results indicate feasibility of reading and face recognition using prosthetic central vision even with 100 μm pixels, and performance improves further with smaller pixels.