Abstract：
BACKGROUND: Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role that image processing and machine learning techniques play in this evolution.
METHODS: We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users.
RESULTS: Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted-normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not.
CONCLUSIONS: This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment.
METHODS: We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users.
RESULTS: Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted-normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not.
CONCLUSIONS: This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment.
摘要：
背景：视网膜假体通过刺激剩余的视网膜细胞部分恢复视力，为患有退行性视网膜疾病的个体提供了希望。这篇综述深入研究了视网膜假体技术的当前进展，特别强调图像处理和机器学习技术在这一演变中发挥的关键作用。
方法：我们对现有的可植入装置和光遗传学策略进行了全面分析，描绘他们的优势，局限性,以及解决复杂视觉任务的挑战。该评论扩展到已实施以增强视网膜假体设备的功能的各种图像处理算法和深度学习架构。我们还通过演示临床试验或通过膦模拟使用模拟假肢视觉（SPV）来说明测试结果，这是模拟视网膜假体使用者的视觉感知的一个关键方面。
结果：我们的综述强调了视网膜假体技术的重大进展，特别是它增强视障人士视觉感知的能力。它讨论了图像处理和深度学习之间的集成，通过应用临床试验说明它们对环境中的个体相互作用和导航的影响，并说明与当前设备一起使用的某些技术的局限性，因为有些方法只使用模拟，即使是对正常的人，或者依赖于定性分析，有些人考虑现实的感知模型，而另一些人则不考虑。
结论：这个跨学科领域对视网膜假体的未来充满希望，具有显着提高视网膜假体患者生活质量的潜力。未来的研究方向应转向优化SPV方法的膦模拟，考虑到磷酸盐感知的扭曲和混乱的性质，从而丰富了由这些假体装置提供的视觉感知。这一努力不仅将提高导航独立性，还将促进与环境的更身临其境的互动。
方法：我们对现有的可植入装置和光遗传学策略进行了全面分析，描绘他们的优势，局限性,以及解决复杂视觉任务的挑战。该评论扩展到已实施以增强视网膜假体设备的功能的各种图像处理算法和深度学习架构。我们还通过演示临床试验或通过膦模拟使用模拟假肢视觉（SPV）来说明测试结果，这是模拟视网膜假体使用者的视觉感知的一个关键方面。
结果：我们的综述强调了视网膜假体技术的重大进展，特别是它增强视障人士视觉感知的能力。它讨论了图像处理和深度学习之间的集成，通过应用临床试验说明它们对环境中的个体相互作用和导航的影响，并说明与当前设备一起使用的某些技术的局限性，因为有些方法只使用模拟，即使是对正常的人，或者依赖于定性分析，有些人考虑现实的感知模型，而另一些人则不考虑。
结论：这个跨学科领域对视网膜假体的未来充满希望，具有显着提高视网膜假体患者生活质量的潜力。未来的研究方向应转向优化SPV方法的膦模拟，考虑到磷酸盐感知的扭曲和混乱的性质，从而丰富了由这些假体装置提供的视觉感知。这一努力不仅将提高导航独立性，还将促进与环境的更身临其境的互动。
