Al Turk Lutfiah, Wang Su, Krause Paul, Wawrzynski James, Saleh George M, Alsawadi Hend, Alshamrani Abdulrahman Zaid, Peto Tunde, Bastawrous Andrew, Li Jingren, Tang Hongying Lilian
Department of Statistics, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.
Department of Computer Science, University of Surrey, Guildford, Surrey, UK.
Transl Vis Sci Technol. 2020 Aug 7;9(2):44. doi: 10.1167/tvst.9.2.44. eCollection 2020 Aug.
The aim of this work is to demonstrate how a retinal image analysis system, DAPHNE, supports the optimization of diabetic retinopathy (DR) screening programs for grading color fundus photography.
Retinal image sets, graded by trained and certified human graders, were acquired from Saudi Arabia, China, and Kenya. Each image was subsequently analyzed by the DAPHNE automated software. The sensitivity, specificity, and positive and negative predictive values for the detection of referable DR or diabetic macular edema were evaluated, taking human grading or clinical assessment outcomes to be the gold standard. The automated software's ability to identify co-pathology and to correctly label DR lesions was also assessed.
In all three datasets the agreement between the automated software and human grading was between 0.84 to 0.88. Sensitivity did not vary significantly between populations (94.28%-97.1%) with specificity ranging between 90.33% to 92.12%. There were excellent negative predictive values above 93% in all image sets. The software was able to monitor DR progression between baseline and follow-up images with the changes visualized. No cases of proliferative DR or DME were missed in the referable recommendations.
The DAPHNE automated software demonstrated its ability not only to grade images but also to reliably monitor and visualize progression. Therefore it has the potential to assist timely image analysis in patients with diabetes in varied populations and also help to discover subtle signs of sight-threatening disease onset.
This article takes research on machine vision and evaluates its readiness for clinical use.
本研究旨在展示视网膜图像分析系统DAPHNE如何支持糖尿病视网膜病变（DR）筛查项目的优化，以对彩色眼底照片进行分级。
从沙特阿拉伯、中国和肯尼亚获取由经过培训和认证的人工分级员分级的视网膜图像集。随后，每个图像由DAPHNE自动化软件进行分析。以人工分级或临床评估结果作为金标准，评估检测可参考性DR或糖尿病性黄斑水肿的灵敏度、特异性、阳性预测值和阴性预测值。还评估了自动化软件识别合并病变和正确标记DR病变的能力。
在所有三个数据集中，自动化软件与人工分级之间的一致性在0.84至0.88之间。不同人群之间的灵敏度没有显著差异（94.28%-97.1%），特异性在90.33%至92.12%之间。所有图像集的阴性预测值均高于93%，表现出色。该软件能够监测基线图像和随访图像之间的DR进展，并可视化这些变化。在可参考的建议中，没有遗漏增殖性DR或糖尿病性黄斑水肿的病例。
DAPHNE自动化软件不仅展示了其对图像进行分级的能力，还展示了其可靠地监测和可视化进展的能力。因此，它有潜力协助对不同人群的糖尿病患者进行及时的图像分析，并有助于发现威胁视力疾病发作的细微迹象。
本文对机器视觉进行了研究，并评估了其临床应用的准备情况。
