Abstract
Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very less human interference. In effect, convolutional neural networks (a deep learning method) have been taught to recognize pathological lesions from images. Diabetes has high morbidity, with millions of people who need to be screened for diabetic retinopathy (DR). Deep neural networks offer a great advantage of screening for DR from retinal images, in improved identification of DR lesions and risk factors for diseases, with high accuracy and reliability. This review aims to compare the current evidences on various deep learning models for diagnosis of diabetic retinopathy (DR).
摘要
生物医学研究的显著进步已经进入大数据时代。利用人工智能使在更短时间内、人工干预更少的情况下从大量数据中提取有意义的信息成为可能。实际上, 脑回神经网络(一种深入的学习方式)已经可以从图像中识别病理病变。糖尿病的发病率很高, 成千上万的人需要进行糖尿病视网膜病变(DR)筛查。深层神经网络在视网膜图像筛查DR中呈现明显优势, 提高了识别DR病灶及疾病危险因素的准确度和可靠性。本文旨在提供和比较目前各种深度学习模式用于糖尿病视网膜病变的诊断的证据。
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Sunny Virmani is an employee of Verily Life Sciences LLC. The authors declare that they have no conflict of interest.
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Raman, R., Srinivasan, S., Virmani, S. et al. Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. Eye 33, 97–109 (2019). https://doi.org/10.1038/s41433-018-0269-y
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