Department of Bio-Medical Physics and Bio-Engineering, Aberdeen University and Grampian University Hospitals, Aberdeen, UK.
Br J Ophthalmol. 2010 Jun;94(6):706-11. doi: 10.1136/bjo.2008.149807. Epub 2009 Aug 5.
BACKGROUND/AIMS: Automated grading has the potential to improve the efficiency of diabetic retinopathy screening services. While disease/no disease grading can be performed using only microaneurysm detection and image-quality assessment, automated recognition of other types of lesions may be advantageous. This study investigated whether inclusion of automated recognition of exudates and haemorrhages improves the detection of observable/referable diabetic retinopathy.
Images from 1253 patients with observable/referable retinopathy and 6333 patients with non-referable retinopathy were obtained from three grading centres. All images were reference-graded, and automated disease/no disease assessments were made based on microaneurysm detection and combined microaneurysm, exudate and haemorrhage detection.
Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% (95% CI 93.5 to 96.0) to 96.6% (95.4 to 97.4) without affecting manual grading workload.
Automated detection of exudates and haemorrhages improved the detection of observable/referable retinopathy.
背景/目的：自动化分级有可能提高糖尿病视网膜病变筛查服务的效率。虽然仅通过微动脉瘤检测和图像质量评估即可进行疾病/无疾病分级，但自动识别其他类型的病变可能更有利。本研究旨在探讨是否纳入自动识别渗出物和出血是否能提高可观察/可转诊糖尿病视网膜病变的检出率。
从三个分级中心获得了 1253 例可观察/可转诊的视网膜病变患者和 6333 例不可转诊的视网膜病变患者的图像。所有图像均经过参考分级，基于微动脉瘤检测和联合微动脉瘤、渗出物和出血检测进行疾病/无疾病的自动评估。
引入渗出物和出血算法后，可观察/可转诊视网膜病变的检出率从 94.9%（95%CI 93.5 至 96.0）显著提高至 96.6%（95.4 至 97.4），而不会影响手动分级工作量。
自动检测渗出物和出血可提高可观察/可转诊的糖尿病视网膜病变的检出率。
