Big data for imaging assessment in glaucoma.

IF 1 Q4 OPHTHALMOLOGY
Taiwan Journal of Ophthalmology Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI:10.4103/tjo.TJO-D-24-00079
Douglas R da Costa, Felipe A Medeiros
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引用次数: 0

Abstract

Glaucoma is the leading cause of irreversible blindness worldwide, with many individuals unaware of their condition until advanced stages, resulting in significant visual field impairment. Despite effective treatments, over 110 million people are projected to have glaucoma by 2040. Early detection and reliable monitoring are crucial to prevent vision loss. With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. Leveraging vast data sources, these technologies promise to enhance clinical practice and public health outcomes by enabling earlier disease detection, progression forecasting, and deeper understanding of underlying mechanisms. This review evaluates the use of Big Data and AI in glaucoma research, providing an overview of most relevant topics and discussing various models for screening, diagnosis, monitoring disease progression, correlating structural and functional changes, assessing image quality, and exploring innovative technologies such as generative AI.

用于青光眼成像评估的大数据。
青光眼是导致全球不可逆转性失明的主要原因,许多人直到晚期才意识到自己的病情,导致严重的视野损伤。尽管有有效的治疗方法,但预计到 2040 年,将有超过 1.1 亿人患有青光眼。早期发现和可靠监测对防止视力丧失至关重要。随着计算技术的快速发展,人工智能(AI)和深度学习(DL)算法正在成为筛查、诊断和监测青光眼进展的潜在工具。利用庞大的数据源,这些技术有望通过更早地发现疾病、预测病情发展和深入了解潜在机制来提高临床实践和公共卫生成果。本综述评估了大数据和人工智能在青光眼研究中的应用,概述了最相关的主题,讨论了用于筛查、诊断、监测疾病进展、关联结构和功能变化、评估图像质量以及探索生成式人工智能等创新技术的各种模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.80
自引率
9.10%
发文量
68
审稿时长
19 weeks
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