Retinopathy of prematurity (ROP) is a severe condition affecting premature
infants, leading to abnormal retinal blood vessel growth, retinal detachment,
and potential blindness. While semi-automated systems have been used in the
past to diagnose ROP-related plus disease by quantifying retinal vessel
features, traditional machine learning (ML) models face challenges like
accuracy and overfitting. Recent advancements in deep learning (DL), especially
convolutional neural networks (CNNs), have significantly improved ROP detection
and classification. The i-ROP deep learning (i-ROP-DL) system also shows
promise in detecting plus disease, offering reliable ROP diagnosis potential.
This research comprehensively examines the contemporary progress and challenges
associated with using retinal imaging and artificial intelligence (AI) to
detect ROP, offering valuable insights that can guide further investigation in
this domain. Based on 89 original studies in this field (out of 1487 studies
that were comprehensively reviewed), we concluded that traditional methods for
ROP diagnosis suffer from subjectivity and manual analysis, leading to
inconsistent clinical decisions. AI holds great promise for improving ROP
management. This review explores AI's potential in ROP detection,
classification, diagnosis, and prognosis.

基于 89 个原始研究的综合分析，我们得出结论，传统的 ROP 诊断方法存在主观性和手动分析的问题，导致临床决策不一致。人工智能在改善 ROP 管理方面有巨大的潜力。本综述研究了人工智能在 ROP 检测、分类、诊断和预后方面的潜力。

人工智能在早产儿视网膜病变中的当前和未来作用

Current and future roles of artificial intelligence in retinopathy of  prematurity

Automatic blood vessel segmentation from retinal images plays an important
role in the diagnosis of many systemic and eye diseases, including retinopathy
of prematurity. Current state-of-the-art research in blood vessel segmentation
from retinal images is based on convolutional neural networks. The solutions
proposed so far are trained and tested on images from a few available retinal
blood vessel segmentation datasets, which might limit their performance when
given an image with retinopathy of prematurity signs. In this paper, we
evaluate the performance of three high-performing convolutional neural networks
for retinal blood vessel segmentation in the context of blood vessel
segmentation on retinopathy of prematurity retinal images. The main motive
behind the study is to test if existing public datasets suffice to develop a
high-performing predictor that could assist an ophthalmologist in retinopathy
of prematurity diagnosis. To do so, we create a dataset consisting solely of
retinopathy of prematurity images with retinal blood vessel annotations
manually labeled by two observers, where one is the ophthalmologist experienced
in retinopathy of prematurity treatment. Experimental results show that all
three solutions have difficulties in detecting the retinal blood vessels of
infants due to a lower contrast compared to images from public datasets as
demonstrated by a significant drop in classification sensitivity. All three
solutions segment alongside retinal also choroidal blood vessels which are not
used to diagnose retinopathy of prematurity, but instead represent noise and
are confused with retinal blood vessels. By visual and numerical observations,
we observe that existing solutions for retinal blood vessel segmentation need
improvement toward more detailed datasets or deeper models in order to assist
the ophthalmologist in retinopathy of prematurity diagnosis.

本文在评估了三种高性能卷积神经网络在分割遗传性视网膜病变视网膜血管图像方面的性能后，得出结论，现有的公共数据集对帮助眼科医生诊断遗传性视网膜病变所需的高性能预测模型存在一定限制，需要更加详细的数据集或更深层的模型。