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视觉评估技术和机器学习在航天相关神经-眼综合征中的地面健康应用

Terrestrial health applications of visual assessment technology and machine learning in spaceflight associated neuro-ocular syndrome.

作者信息

Ong Joshua, Tavakkoli Alireza, Zaman Nasif, Kamran Sharif Amit, Waisberg Ethan, Gautam Nikhil, Lee Andrew G

机构信息

University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.

Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA.

出版信息

NPJ Microgravity. 2022 Aug 25;8(1):37. doi: 10.1038/s41526-022-00222-7.

Abstract

The neuro-ocular effects of long-duration spaceflight have been termed Spaceflight Associated Neuro-Ocular Syndrome (SANS) and are a potential challenge for future, human space exploration. The underlying pathogenesis of SANS remains ill-defined, but several emerging translational applications of terrestrial head-mounted, visual assessment technology and machine learning frameworks are being studied for potential use in SANS. To develop such technology requires close consideration of the spaceflight environment which is limited in medical resources and imaging modalities. This austere environment necessitates the utilization of low mass, low footprint technology to build a visual assessment system that is comprehensive, accessible, and efficient. In this paper, we discuss the unique considerations for developing this technology for SANS and translational applications on Earth. Several key limitations observed in the austere spaceflight environment share similarities to barriers to care for underserved areas on Earth. We discuss common terrestrial ophthalmic diseases and how machine learning and visual assessment technology for SANS can help increase screening for early intervention. The foundational developments with this novel system may help protect the visual health of both astronauts and individuals on Earth.

摘要

长期太空飞行的神经眼科效应被称为太空飞行相关神经眼科综合征(SANS),是未来人类太空探索面临的一个潜在挑战。SANS的潜在发病机制仍不明确,但目前正在研究地面头戴式视觉评估技术和机器学习框架的几种新兴转化应用,以探讨其在SANS中的潜在用途。要开发此类技术,需要密切考虑医疗资源和成像方式有限的太空飞行环境。这种严峻的环境需要利用低质量、小体积的技术来构建一个全面、易用且高效的视觉评估系统。在本文中,我们讨论了为SANS开发此类技术以及在地球上进行转化应用时需要考虑的独特因素。在严峻的太空飞行环境中观察到的几个关键限制与地球上医疗服务不足地区的护理障碍有相似之处。我们讨论了常见的地面眼科疾病,以及用于SANS的机器学习和视觉评估技术如何有助于增加早期干预筛查。这种新型系统的基础发展可能有助于保护宇航员和地球上人们的视觉健康。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3691/9411571/7ff6379e185b/41526_2022_222_Fig1_HTML.jpg

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