Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning.
Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning.
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DOI：
10.3390/diagnostics11111943
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发表时间：
2021-10-20
期刊：
影响因子：
--
通讯作者：
Sivaprasad S
中科院分区：
文献类型：
--
作者：
Cervera DR;Smith L;Diaz-Santana L;Kumar M;Raman R;Sivaprasad S
本研究的目的是开发和验证基于深度学习的系统，以从糖尿病患者的视网膜彩色图像中检测周围神经病变 (DN)。 1561 名糖尿病患者的视网膜图像被用来预测根据振动感知阈值诊断的 DN。共有 189 人患有糖尿病视网膜病变 (DR)，276 人患有 DN，43 人同时患有 DR 和 DN。 90% 的图像用于训练和验证，10% 用于测试。利用了深度神经网络，包括 Squeezenet、Inception 和 Densenet，并在使用和不使用预先训练的权重的情况下对架构进行了测试。训练期间使用图像的随机变换。该算法使用三组数据进行训练和测试：所有视网膜图像、无 DR 的图像和有 DR 的图像。 ROC 曲线下面积 (AUC) 用于评估性能。预测整个队列中 DN 的 AUC 在验证集上为 0.8013 (±0.0257)，在测试集上为 0.7097 (±0.0031)。存在 DR 时，AUC 增加至 0.8673 (±0.0088)。视网膜图像可用于识别 DN 患者，并为患者提供在参加 DR 筛查时了解其 DN 状况的机会。
The aim of this study was to develop and validate a deep learning-based system to detect peripheral neuropathy (DN) from retinal colour images in people with diabetes. Retinal images from 1561 people with diabetes were used to predictDN diagnosed on vibration perception threshold. A total of 189 had diabetic retinopathy (DR), 276 had DN, and 43 had both DR and DN. 90% of the images were used for training and validation and 10% for testing. Deep neural networks, including Squeezenet, Inception, and Densenet were utilized, and the architectures were tested with and without pre-trained weights. Random transform of images was used during training. The algorithm was trained and tested using three sets of data: all retinal images, images without DR and images with DR. Area under the ROC curve (AUC) was used to evaluate performance. The AUC to predict DN on the whole cohort was 0.8013 (±0.0257) on the validation set and 0.7097 (±0.0031) on the test set. The AUC increased to 0.8673 (±0.0088) in the presence of DR. The retinal images can be used to identify individuals with DN and provides an opportunity to educate patients about their DN status when they attend DR screening.
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影响因子：
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作者：
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