Visual prostheses: the enabling technology to give sight to the blind

Millions of patients are either slowly losing their vision or are already blind due to retinal degenerative diseases such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD) or because of accidents or injuries. Employment of artificial means to treat extreme vision impairment has come closer to reality during the past few decades. Currently, many research groups work towards effective solutions to restore a rudimentary sense of vision to the blind. Aside from the efforts being put on replacing damaged parts of the retina by engineered living tissues or microfabricated photoreceptor arrays, implantable electronic microsystems, referred to as visual prostheses, are also sought as promising solutions to restore vision. From a functional point of view, visual prostheses receive image information from the outside world and deliver them to the natural visual system, enabling the subject to receive a meaningful perception of the image. This paper provides an overview of technical design aspects and clinical test results of visual prostheses, highlights past and recent progress in realizing chronic high-resolution visual implants as well as some technical challenges confronted when trying to enhance the functional quality of such devices.

PVGAN: A generative adversarial network for object simplification in prosthetic vision

OBJECTIVE

By means of electrical stimulation of the visual system, visual prostheses provide promising solution for blind patients through partial restoration of their vision. Despite the great success achieved so far in this field, the limited resolution of the perceived vision using these devices hinders the ability of visual prostheses users to correctly recognize viewed objects. Accordingly, we propose a deep learning approach based on Generative Adversarial Networks (GANs), termed PVGAN, to enhance object recognition for the implanted patients by representing objects in the field of view based on a corresponding simplified clip art version.

APPROACH

To assess the performance, an axon map model was used to simulate prosthetic vision in experiments involving normally-sighted participants. In these experiments, four types of image representation were examined. The first and second types comprised presenting phosphene simulation of real images containing the actual high-resolution object, and presenting phosphene simulation of the real image followed by the clip art image, respectively. The other two types were utilized to evaluate the performance in the case of electrode dropout, where the third type comprised presenting phosphene simulation of only clip art images without electrode dropout, while the fourth type involved clip art images with electrode dropout.

MAIN RESULTS

The performance was measured through three evaluation metrics which are the accuracy of the participants in recognizing the objects, the time taken by the participants to correctly recognize the object, and the confidence level of the participants in the recognition process. Results demonstrate that representing the objects using clip art images generated by the PVGAN model results in a significant enhancement in the speed and confidence of the subjects in recognizing the objects.

SIGNIFICANCE

These results demonstrate the utility of using GANs in enhancing the quality of images perceived using prosthetic vision.