The impact of artificial intelligence in the diagnosis and management of glaucoma

Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform the field of glaucoma. Autonomous DL algorithms are capable of maximizing information embedded in digital fundus photographs and ocular coherence tomographs to outperform ophthalmologists in disease detection. Other unsupervised algorithms such as principal component analysis (axis learning) and archetypal analysis (corner learning) facilitate visual field interpretation and show great promise to detect functional glaucoma progression and differentiate it from non-glaucomatous changes when compared with conventional software packages. Forecasting tools such as the Kalman filter may revolutionize glaucoma management by accounting for a host of factors to set target intraocular pressure goals that preserve vision. Activation maps generated from DL algorithms that process glaucoma data have the potential to efficiently direct our attention to critical data elements embedded in high throughput data and enhance our understanding of the glaucomatous process. It is hoped that AI will realize more accurate assessment of the copious data encountered in glaucoma management, improving our understanding of the disease, preserving vision, and serving to enhance the deep bonds that patients develop with their treating physicians. 深度学习 (Deep learning,DL) 是人工智能的一个分支, 通过模仿哺乳动物视皮层合成影像的能力, 建立多层神经网络模型。 这种技术会在青光眼领域起到变革的作用。自主DL算法能够最大化地收集眼底图像和OCT中包含的信息, 在疾病探查方面甚至能够超越眼科医生。其他的无监管算法例如主成分分析 (纵学习) 和原型分析 (角点学习) 有助于对视野结果进行解释, 并且与传统软件包相比能够更好地检测青光眼功能性进展, 并与非青光眼相鉴别。此外, 如卡尔曼滤波器等预测性工具可收录一系列影响因素后确定维持视力的目标眼压值, 从而彻底改变了青光眼的管理。DL算法通过处理青光眼数据可生成激活图, 引导我们关注高通量数据中嵌入的关键数据元素, 并加强我们对青光眼发展过程的理解。最后, 希望AI能够更加精准地评估青光眼治疗管理过程中的大量数据, 提高我们对青光眼、保护视力的认识, 成为患者与医生之间的深厚纽带。