When it comes to bioelectronics, university researchers are continually developing technology to improve people's quality of living with various issues like paralysis, deafness, and blindness.
One way to combat vision loss, in particular, is artificial retinas—one of the first instances being the Argus II implant in 2007. As of 2018, .
The Argus II is a byproduct of more than 10 years of R&D. Image used courtesy of
Recently, several universities have approached artificial retinas separately, publishing lab-based research on technology that could lead to human trials in the future.
Basic Principles for Artificial Retinas
To understand the basic principles underlying artificial retinas, it may be useful to first assess the working principles of Argus II from Second Sight Medical, the .
Electrical impulses are generated based upon post-processing data from a glasses-based camera system, which is then impressed upon electrodes that have been surgically attached inside a patient's retina.
A high-level diagram of how a retina implant works. Image used courtesy of
These electrical signals interface with the remaining biological “circuitry,” the ganglion clusters, in the eye to relay stimulus to the brain. Much of the research coming down from universities seem to model similar parameters designed to combat retinitis pigmentosa.
For example, researchers at EPFL (École Polytechnique Fédérale de Lausanne) in Switzerland have been , providing a high-resolution electrode array with a field of vision of nearly 43 degrees ().
The (a) high-density prosthesis designed by researchers at EPFL, with an active working area of 13 mm in diameter, contains nearly (b) 10,500 photovoltaic pixels. Image used courtesy of .
Stanford Focuses on Electrode Density
Stanford researchers claim that current solutions on the market, namely the Argus II, fail to address a major issue in ganglion cell activation.
The Stanford research indicates that various ganglion cell clusters are responsible for different visual aspects and must be activated individually. Image courtesy of
The large electrodes currently in use, between 50 μm to 500 μm, activate cell clusters in aggregate, which confuses the brain's signals. This results in limited restoration of vision.
Stanford’s system for an artificial retina, which shares characteristic design elements to alternatives, such as glasses with a built-in camera, processing capability, and wireless transfer to the retina. Image used courtesy of
Instead of these large electrodes, the researchers that can excite a small number of cells in the epiretinal region of the eye (where the mostly intact ganglion reside).
Is Ocular Mimicry Sufficient to Replace Eyes?
The Hong Kong University of Science and Technology (HKUST) researchers have taken a distinctly divergent path to the previous research candidates and .
The researchers from HKUST believe this lens structure sharpens the lens's focus due to the dome shape reducing the spreading effect of light compared to a flat sensor (as in image cameras), much like the human eye.
Last May, the journal Nature provided a commentary report on HKUST's research into the bionic eye. The study indicated that .
This sensitivity is principally due to the novel use of perovskite materials, which have favorable properties in the next-generation of optoelectronic signals.
HKUST's conceptual design of a bionic eye. Image used courtesy of
The nanowires present inside the artificial retina act as photoreceptors to relay information along the liquid-metal wires (analogous to nerve fibers). In a video released from HKUST, researchers demonstrated how the bionic eye possesses photoreception capability, namely an impressive despite the limited ocular resolution.
Overcoming Design Challenges Today Mean Clearer Vision Tomorrow
For these research groups, overcoming both design and biological challenges could mean a future where functional vision is possible for everyone.
Aside from the long wait times for medical trials in humans, the research at EPFL is facing fabrication issues with pixels cracking during bonding to the hemispherical shape. According to the research team, additional issues, such as the potential of crosstalk between pixels and the requirement of stimulation of single ganglion cells by single pixels, are also constraints.
Stanford acknowledges a series of questions that need answering to move forward their research, many of which are centered around encoding signals to the brain and the specific activation patterns of ganglion cells. They hope to model some of these problems with advancements in machine learning.
Finally, the team at HKUST has several issues to overcome, including an expensive fabrication process and a limited eye resolution—a large 200 μm gap between the pixels, which limits the light detection region.
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