Towards a Smart Bionic Eye: AI-Powered Artificial Vision for the Treatment of Incurable Blindness

迈向智能仿生眼：人工智能驱动的人工视觉治疗不可治愈的失明

• 批准号: 10473346
• 负责人: Michael Beyeler
• 金额: $ 125.01万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10473346
• 关键词: Address; Adopted; Affect; Artificial Intelligence; Bionics; Blindness; Brain; Code; Development; Devices; Electric Stimulation; Electrodes; Eye; Face; Genes; Goals; Human; Neurosciences; Ocular Prosthesis; Optic Nerve; Perception; Persons; Photic Stimulation; Quality of life; Self Care; Shapes; System; Technology; Thinking; Translating; Vision; Visual; Visual Aid; Visual impairment; Visual system structure; Visually Impaired Persons; Work; base; blind; computational neuroscience; computer human interaction; computer science; data-driven model; effective therapy; improved; insight; neural stimulation; neuroprosthesis; next generation; prototype; retinal damage; retinal stimulation; sensory substitution; stem cell therapy

Towards a Smart Bionic Eye: AI-Powered Artificial Vision for the Treatment of Incurable Blindness How can we return a functional form of sight to people who are living with incurable blindness? Despite recent advances in gene and stem cell therapies are showing great promise, there are no effective treatments for many people blinded by severe degeneration or damage to the retina, the optic nerve, or cortex. In such cases, an electronic visual prosthesis (“bionic eye”) may be the only option. However, the quality of current prosthetic vision is still rudimentary and does not differ much across different device technologies. A major outstanding challenge is translating electrode stimulation into a code that the brain can understand. Rather than aiming to one day restore natural vision with visual prostheses, we might be better off thinking about how to create practical and useful artificial vision. Specifically, a visual prosthesis has the potential to provide visual augmentations through the means of artificial intelligence (AI) based scene understanding, tailored to specific real-world tasks that are known to affect the quality of life of people who are blind (e.g., face recognition, outdoor navigation, self-care). The goal of this proposal is thus to address fundamental questions at the intersection of neuroscience, computer science, and human-computer interaction that will enable the development of a Smart Bionic Eye; that is, a visual neuroprosthesis that functions as an AI-powered visual aid by providing visual augmentations to support specific everyday tasks. To enable such a technology, we first need to 1) understand how visual prostheses interact with the human visual system to shape perception, 2) identify visual augmentation strategies that best support specific real-world tasks, and 3) develop a prototyping system that allows us to validate as well as iteratively improve upon our augmentation strategies with the bionic eye recipient in the loop. This work will further our understanding of how brain stimulation leads to perception, and the insights gained from identifying optimal visual augmentation strategies may be broadly applicable to different visual aids and sensory substitution devices, therefore potentially benefitting both people who are blind and people with low vision. Last but not least, the ability of a visual prosthesis to support everyday tasks might make the difference between abandoned technology and a widely adopted next-generation neuroprosthetic device.

朝着智能的仿生眼：AI驱动的人造视力治疗无法治愈的失明 我们如何将视力形式的视线归还给无法治愈的失明的人？尽管最近 基因和干细胞疗法的进步表现出巨大的希望，许多人没有有效的治疗方法 因严重的变性或视网膜，视神经或皮层损害而蒙蔽的人。在这种情况下， 电子视觉假体（“仿生眼”）可能是唯一的选择。但是，当前的假肢质量 在不同的设备技术中仍然是基本的，并且没有太大不同。一个主要的挑战 正在将电极刺激转化为大脑可以理解的代码。而不是瞄准一天 通过视觉假体恢复自然视觉，我们可能会更好地考虑如何创建实用和 有用的人造视力。具体而言，视觉假体有可能通过 基于人工智能（AI）的场景理解的手段，量身定制为特定的现实世界任务 已知会影响盲人的人的生活质量（例如，面部识别，户外导航，自我护理）。 因此，该提案的目的是在神经科学，计算机的交集中解决基本问题 科学和人类计算机的互动将使智能仿生的眼睛的发展；也就是说，视觉 神经假体通过提供视觉增强来支持特定 日常任务。要启用这样的技术，我们首先需要1）了解视觉假体如何与 人类视觉系统以塑造感知，2）确定最能特定支持的视觉增强策略 现实世界任务，以及3）开发一个原型制度，使我们能够验证并迭代改进 在我们的增强策略下，循环中的仿生型眼睛接受者。 这项工作将进一步理解大脑刺激如何导致感知的理解，并获得了见解 从确定最佳视觉增强策略可以广泛适用于不同的视觉辅助工具和 感觉替代设备，因此有可能使两个盲人和低人的人受益 想象。最后但并非最不重要的一点是，视觉假体支持每天任务的能力可能会使您有所不同 在废弃的技术和广泛采用的下一代神经假体装置之间。