Retinopathy of prematurity (ROP) is a potentially blinding disease in premature neonates that requires a skilled workforce for diagnosis, monitoring, and treatment. Artificial intelligence is a valuable tool that clinicians employ to reduce the screening burden on ophthalmologists and neonatologists and improve the detection of treatment-requiring ROP. Neural networks such as convolutional neural networks and deep learning (DL) systems are used to calculate a vascular severity score (VSS), an important component of various risk models. These DL systems have been validated in various studies, which are reviewed here. Most importantly, we discuss a promising study that validated a DL system that could predict the development of ROP despite a lack of clinical evidence of disease on the first retinal examination. Additionally, there is promise in utilizing these systems through telemedicine in more rural and resource-limited areas. This review highlights the value of these DL systems in early ROP diagnosis.

中文翻译：

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人工智能在早产儿视网膜病变早期检测中的应用：文献综述。

早产儿视网膜病变 (ROP) 是早产儿的一种潜在致盲疾病，需要熟练的工作人员进行诊断、监测和治疗。人工智能是一种宝贵的工具，临床医生可以用来减轻眼科医生和新生儿科医生的筛查负担，并提高对需要治疗的 ROP 的检测。卷积神经网络和深度学习 (DL) 系统等神经网络用于计算血管严重程度评分 (VSS)，这是各种风险模型的重要组成部分。这些深度学习系统已在各种研究中得到验证，本文对此进行了综述。最重要的是，我们讨论了一项有前途的研究，该研究验证了 DL 系统，该系统可以预测 ROP 的发展，尽管在第一次视网膜检查时缺乏疾病的临床证据。此外，在更多农村和资源有限的地区通过远程医疗利用这些系统也有希望。本综述强调了这些深度学习系统在早期 ROP 诊断中的价值。