Department of Ophthalmology and Visual Sciences, University of Iowa, 11290C PFP UIHC, 200 Hawkins Drive, Iowa City, IA 52242, USA.
Expert Rev Med Devices. 2010 Mar;7(2):287-96. doi: 10.1586/erd.09.76.
Automated identification of diabetic retinopathy (DR), the primary cause of blindness and visual loss for those aged 18-65 years, from color images of the retina has enormous potential to increase the quality, cost-effectiveness and accessibility of preventative care for people with diabetes. Through advanced image analysis techniques, retinal images are analyzed for abnormalities that define and correlate with the severity of DR. Translating automated DR detection into clinical practice will require surmounting scientific and nonscientific barriers. Scientific concerns, such as DR detection limits compared with human experts, can be studied and measured. Ethical, legal and political issues can be addressed, but are difficult or impossible to measure. The primary objective of this review is to survey the methods, potential benefits and limitations of automated detection in order to better manage translation into clinical practice, based on extensive experience with the systems we have developed.
从视网膜的彩色图像中自动识别糖尿病视网膜病变（DR），这是 18-65 岁人群失明和视力丧失的主要原因，具有极大的潜力提高糖尿病患者预防保健的质量、成本效益和可及性。通过先进的图像分析技术，对视网膜图像进行分析，以识别和关联 DR 严重程度的异常。将自动 DR 检测转化为临床实践将需要克服科学和非科学的障碍。科学方面的担忧，例如与人类专家相比的 DR 检测限制，可以进行研究和测量。伦理、法律和政治问题可以得到解决，但难以或不可能进行衡量。本综述的主要目的是调查自动检测的方法、潜在的益处和局限性，以便更好地管理基于我们所开发的系统的转化为临床实践，这是基于我们的广泛经验。
