Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
Sci Rep. 2023 Jun 6;13(1):9165. doi: 10.1038/s41598-023-36327-6.
Diabetic retinopathy is a leading cause of blindness in working-age adults worldwide. Neovascular leakage on fluorescein angiography indicates progression to the proliferative stage of diabetic retinopathy, which is an important distinction that requires timely ophthalmic intervention with laser or intravitreal injection treatment to reduce the risk of severe, permanent vision loss. In this study, we developed a deep learning algorithm to detect neovascular leakage on ultra-widefield fluorescein angiography images obtained from patients with diabetic retinopathy. The algorithm, an ensemble of three convolutional neural networks, was able to accurately classify neovascular leakage and distinguish this disease marker from other angiographic disease features. With additional real-world validation and testing, our algorithm could facilitate identification of neovascular leakage in the clinical setting, allowing timely intervention to reduce the burden of blinding diabetic eye disease.
糖尿病性视网膜病变是全球导致工作年龄成年人失明的主要原因。荧光素血管造影上的新生血管渗漏表明糖尿病性视网膜病变已进展至增殖期，这是一个重要的区别，需要及时进行眼科干预，如激光或眼内注射治疗，以降低严重、永久性视力丧失的风险。在这项研究中，我们开发了一种深度学习算法，用于检测从糖尿病性视网膜病变患者获得的超广角荧光素血管造影图像上的新生血管渗漏。该算法是三个卷积神经网络的集合，能够准确地对新生血管渗漏进行分类，并将其与其他血管造影疾病特征区分开来。通过进一步的真实世界验证和测试，我们的算法可以帮助在临床环境中识别新生血管渗漏，以便及时进行干预，减轻致盲性糖尿病眼病的负担。
