Sarbout Ilias, Gungor Ayse, Ounissi Mehdi, Zaher Samy, Ptito Maurice, Kupers Ron, Racoceanu Daniel, Milea Dan
Department of Neuro-Ophthalmology, Rothschild Foundation Hospital, Paris, France.
Rothschild Computational and Visual Neurosciences BRAIN Laboratory, Rothschild Foundation Hospital, Paris, France.
Eye Brain. 2025 Aug 29;17:95-113. doi: 10.2147/EB.S524322. eCollection 2025.
Over the past few decades, technological advancements have transformed invasive visual prostheses from theoretical concepts into real-world applications. However, functional outcomes remain limited, especially in visual acuity. This review aims to summarize current developments in retinal and cortical prostheses (RCPs) and critically assess the role of artificial intelligence (AI) in advancing these systems.
To describe current RCPs and provide a systematic review on image and signal processing algorithms designed for improved clinical outcomes.
We performed a systematic review of the literature related to AI subserving prosthetic vision, using mainly PubMed, but also, Elicit, a dedicated AI-based reference research assistant. A total of 455 studies were screened on PubMed, of which 23 were retained for inclusion. An additional 5 studies were identified and included through Elicit.
The analysis of current RCPs highlights various limitations affecting the quality of the visual flow provided by current artificial vision. Indeed, the 28 reviewed studies on AI covered two applications for RCPs including extraction of saliency in camera captured images, and consistency between electrical stimulation and perceived phosphenes. A total of 14 out of 28 studies involved the use of artificial neural networks, of which 12 included model training. Evaluation with data from a visual prosthesis was conducted in 7 studies, including 1 that was prospectively assessed with a human RCP. Validation with empirical data from human or animal data was performed in 22 out of 28 studies. Out of these, 15 were validated using simulated prosthetic vision. Finally, out of 22 studies leveraging a mathematical model for phosphenes perception, 14 used a symmetrical oversimplified modeling.
AI algorithms show promise in optimizing prosthetic vision, particularly through enhanced image saliency extraction and stimulation strategies. However, most current studies are based on simulations. Further development and validation in real-world settings, especially through clinical testing with blind patients, are essential to assess their true effectiveness.
在过去几十年中，技术进步已将侵入性视觉假体从理论概念转变为实际应用。然而，功能结果仍然有限，尤其是在视力方面。本综述旨在总结视网膜和皮质假体（RCP）的当前发展情况，并批判性地评估人工智能（AI）在推进这些系统中的作用。
描述当前的RCP，并对为改善临床结果而设计的图像和信号处理算法进行系统综述。
我们对与辅助假体视觉的AI相关的文献进行了系统综述，主要使用PubMed，还使用了基于AI的专用参考研究助手Elicit。在PubMed上共筛选了455项研究，其中23项被保留纳入。通过Elicit又识别并纳入了5项研究。
对当前RCP的分析突出了影响当前人工视觉所提供视觉流质量的各种限制。实际上，28项关于AI的综述研究涵盖了RCP的两种应用，包括相机捕获图像中显著特征的提取，以及电刺激与感知光幻视之间的一致性。28项研究中有14项涉及人工神经网络的使用，其中12项包括模型训练。7项研究使用视觉假体数据进行了评估，其中1项对人类RCP进行了前瞻性评估。28项研究中有22项使用来自人类或动物数据的经验数据进行了验证。其中，15项使用模拟假体视觉进行了验证。最后，在22项利用光幻视感知数学模型的研究中，14项使用了对称的过度简化模型。
AI算法在优化假体视觉方面显示出前景，特别是通过增强图像显著特征提取和刺激策略。然而，目前大多数研究基于模拟。在实际环境中进行进一步的开发和验证，特别是通过对盲人患者的临床试验，对于评估其真正有效性至关重要。
