PDF(Pubmed)
Abstract：
Over the past decade, artificial intelligence (AI) and its subfields, deep learning and machine learning, have become integral parts of ophthalmology, particularly in the field of ophthalmic imaging. A diverse array of algorithms has emerged to facilitate the automated diagnosis of numerous medical and surgical retinal conditions. The development of these algorithms necessitates extensive training using large datasets of retinal images. This approach has demonstrated a promising impact, especially in increasing accuracy of diagnosis for unspecialized clinicians for various diseases and in the area of telemedicine, where access to ophthalmological care is restricted. In parallel, robotic technology has made significant inroads into the medical field, including ophthalmology. The vast majority of research in the field of robotic surgery has been focused on anterior segment and vitreoretinal surgery. These systems offer potential improvements in accuracy and address issues such as hand tremors. However, widespread adoption faces hurdles, including the substantial costs associated with these systems and the steep learning curve for surgeons. These challenges currently constrain the broader implementation of robotic surgical systems in ophthalmology. This mini review discusses the current research and challenges, underscoring the limited yet growing implementation of AI and robotic systems in the field of retinal conditions.
摘要：
在过去的十年里，人工智能(AI)及其子领域，深度学习和机器学习，已经成为眼科不可或缺的一部分，尤其是在眼科成像领域。已经出现了各种各样的算法阵列以促进许多医学和外科视网膜状况的自动诊断。这些算法的发展需要使用大的视网膜图像数据集进行广泛的训练。这种方法显示出了有希望的影响，特别是在提高非专业临床医生对各种疾病的诊断准确性和远程医疗领域，眼科护理受到限制。并行,机器人技术在医疗领域取得了重大进展，包括眼科.机器人手术领域的绝大多数研究都集中在眼前节和玻璃体视网膜手术上。这些系统在准确性方面提供了潜在的改进，并解决了诸如手抖等问题。然而,广泛采用面临障碍，包括与这些系统相关的大量成本和外科医生陡峭的学习曲线。这些挑战目前限制了机器人手术系统在眼科中的更广泛实施。这篇小型评论讨论了当前的研究和挑战，强调人工智能和机器人系统在视网膜疾病领域的实施有限但不断增长。
