Bajwa Awais, Nosheen Neelam, Talpur Khalid Iqbal, Akram Sheeraz
Ophthalytics, Marietta, GA 30062, USA.
Sindh Institute of Ophthalmology & Visual Sciences (SIOVS), Hyderabad 71000, Pakistan.
Diagnostics (Basel). 2023 Jan 20;13(3):393. doi: 10.3390/diagnostics13030393.
Diabetic Retinopathy (DR) is the most common complication that arises due to diabetes, and it affects the retina. It is the leading cause of blindness globally, and early detection can protect patients from losing sight. However, the early detection of Diabetic Retinopathy is an difficult task that needs clinical experts' interpretation of fundus images. In this study, a deep learning model was trained and validated on a private dataset and tested in real time at the Sindh Institute of Ophthalmology & Visual Sciences (SIOVS). The intelligent model evaluated the quality of the test images. The implemented model classified the test images into DR-Positive and DR-Negative ones. Furthermore, the results were reviewed by clinical experts to assess the model's performance. A total number of 398 patients, including 232 male and 166 female patients, were screened for five weeks. The model achieves 93.72% accuracy, 97.30% sensitivity, and 92.90% specificity on the test data as labelled by clinical experts on Diabetic Retinopathy.
糖尿病性视网膜病变（DR）是糖尿病引发的最常见并发症，它会影响视网膜。它是全球失明的主要原因，早期检测可防止患者失明。然而，糖尿病性视网膜病变的早期检测是一项艰巨任务，需要临床专家对眼底图像进行解读。在本研究中，一个深度学习模型在一个私有数据集上进行了训练和验证，并在信德眼科与视觉科学研究所（SIOVS）进行了实时测试。该智能模型评估了测试图像的质量。所实施的模型将测试图像分为DR阳性和DR阴性。此外，临床专家对结果进行了审查，以评估该模型的性能。总共对398名患者进行了为期五周的筛查，其中包括232名男性患者和166名女性患者。根据临床专家对糖尿病性视网膜病变的标注，该模型在测试数据上的准确率达到93.72%，灵敏度达到97.30%，特异性达到92.90%。
