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.

迈向智能仿生眼：人工智能驱动的人工视觉用于治疗无法治愈的失明 我们怎样才能让那些患有无法治愈的失明的人恢复正常的视力呢？尽管最近 基因和干细胞疗法的进展显示出巨大的希望，但许多人没有有效的治疗方法。 因视网膜、视神经或皮层严重退化或损伤而失明的人。在这种情况下， 电子视觉假体（“仿生眼”）可能是唯一的选择。然而，目前的假肢视力的质量 仍然是初级的，并且在不同的设备技术之间没有太大的差异。一项重大的、悬而未决的挑战 将电极刺激转化为大脑可以理解的代码。与其说有一天 用视觉假体恢复自然视力，我们最好考虑如何创造实用的， 有用的人工视觉具体来说，视觉假体具有通过以下方式提供视觉增强的潜力： 基于人工智能（AI）的场景理解手段，针对特定的现实世界任务量身定制， 已知会影响盲人的生活质量（例如，面部识别、户外导航、自我护理）。 因此，本提案的目标是解决神经科学、计算机科学和计算机科学交叉点的基本问题。 科学和人机交互，这将使智能仿生眼的发展;也就是说，一个视觉 神经假体，通过提供视觉增强来支持特定的视觉辅助， 日常任务。为了实现这样的技术，我们首先需要1）了解视觉假体如何与 人类视觉系统的形状感知，2）确定视觉增强策略，最好地支持特定的 真实世界的任务，以及3）开发一个原型系统，使我们能够验证以及迭代改进 仿生眼接受者参与的增强策略 这项工作将进一步加深我们对大脑刺激如何导致感知的理解， 从识别最佳视觉增强策略可以广泛地适用于不同的视觉辅助， 因此，感官替代装置可能使盲人和低血糖患者受益， 视野最后但并非最不重要的是，视觉假体支持日常任务的能力可能会有所不同 废弃的技术和广泛采用的下一代神经假体装置之间的联系。