School of Computer Science and Engineering, University of New South Wales, Kensington, New South Wales, Australia.
School of Optometry and Vision Science, University of New South Wales, Kensington, Australia.
Clin Exp Optom. 2024 Mar;107(2):130-146. doi: 10.1080/08164622.2023.2235346. Epub 2023 Sep 6.
Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning - two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence - has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67-98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71-0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.
人工智能是计算机科学中一个快速发展的领域，涵盖了机器对人类智能的模拟。机器学习和深度学习——人工智能下的两个主要数据驱动模式分析方法——在过去几十年中引起了相当大的兴趣。技术的发展导致了大量使用视网膜图像对眼科和神经退行性疾病进行诊断的人工智能研究。已经使用了各种基于人工智能的技术用于诊断目的，包括传统机器学习、深度学习及其组合。本文回顾了过去 10 年关于这个主题的文献，讨论了人工智能在分析来自不同模式及其组合的数据方面的应用，用于诊断青光眼和神经退行性疾病。由于多种因素的影响，发表的人工智能方法的性能存在差异，但结果表明，这些方法可能有助于临床诊断。一般来说，人工智能辅助诊断的准确率范围为 67-98%，敏感性特异性曲线下面积（AUC）范围为 0.71-0.98，优于典型的人类准确率 71.5%和曲线下面积 0.86。这表明基于人工智能的工具可以为临床医生提供有用的信息，有助于提供改进的诊断。综述表明，在将基于人工智能的模型整合到临床实践之前，使用视网膜成像模式可以改进现有的基于人工智能的模型。
