AI-Powered Artificial Vision for Visual Prostheses

Condition(s) studied

Investigational drug(s) / intervention(s)

Visual prosthesis: In response to the stimulation/image on the monitor, subjects will be asked to either make a perceptual judgment or perform a simple behavioral task. Examples include detecting a stimulus ('did you see a light on that trial'), reporting size by drawing on a touch screen, or walking to a target location. Both patient response and reaction time will be recorded. In some cases, the investigators will also collect data measuring subjects' eye position. This is a noninvasive procedure that will be carried out using standard eye-tracking equipment via an infra-red camera that tracks the position of the subjects' pupil. Only measurements like eye position or eye blinks will be recorded, so these data do not contain identifiable information.

Study summary

Visual impairment is one of the ten most prevalent causes of disability and poses extraordinary challenges to individuals in our society that relies heavily on sight. Living with acquired blindness not only lowers the quality of life of these individuals, but also strains society's limited resources for assistance, care and rehabilitation. However, to date, there is no effective treatment for man patients who are visually handicapped as a result of degeneration or damage to the inner layers of the retina, the optic nerve or the visual pathways. Therefore, there are compelling reasons to pursue the development of a cortical visual prosthesis capable of restoring some useful sight in these profoundly blind patients.

However, the quality of current prosthetic vision is still rudimentary. A major outstanding challenge is translating electrode stimulation into a code that the brain can understand. Interactions between the device electronics and the retinal neurophysiology lead to distortions that can severely limit the quality of the generated visual experience. Rather than aiming to one day restore natural vision (which may remain elusive until the neural code of vision is fully understood), one might be better off thinking about how to create practical and useful artificial vision now.

The goal of this work is to address fundamental questions that will allow the development of a Smart Bionic Eye, a device that relies on AI-powered scene understanding to augment the visual scene (similar to the Microsoft HoloLens), tailored to specific real-world tasks that are known to diminish the quality of life of people who are blind (e.g., face recognition, outdoor navigation, reading, self-care).

Eligibility

Primary outcome measure(s)

• Phosphene shape — through study completion, an average of 1 year
  The effect of stimulation strategy on the shape of phosphenes elicited by electrical stimulation. Phosphene shape will be recorded via participant drawings on a touchscreen and quantified using image moments (e.g., area, orientation, eccentricity).
• Pattern discrimination accuracy — through study completion, an average of 1 year
  The effect of stimulation strategy on the ability to discriminate patterns (accuracy, precision, recall) elicited by electrical stimulation as assessed by verbal responses
• Scene understanding performance — through study completion, an average of 1 year
  The effect of stimulation strategy to locate objects of interest (accuracy, precision, recall) and relay accurate descriptions of the visual scene as assessed by verbal responses

Trial sites (2)