assessment of automated disease detection in diabetic retinopathy screening using two-field photography自动化的评估疾病检测在糖尿病性视网膜病变筛查使用两个区域摄影.pdf
文本预览下载声明
Assessment of Automated Disease Detection in Diabetic
Retinopathy Screening Using Two-Field Photography
1 2 2 2
Keith Goatman , Amanda Charnley , Laura Webster , Stephen Nussey *
1 School of Medicine and Dentistry, University of Aberdeen, Aberdeen, Scotland, 2 St. George’s Hospital, London, United Kingdom
Abstract
Aim: To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic
photography.
Methods: Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening
service were processed by the Medalytix iGradingTM automated grading system. For each screening episode macular-
centred and disc-centred images of both eyes were acquired and independently graded according to the English national
grading scheme. Where discrepancies were found between the automated result and original manual grade, internal and
external arbitration was used to determine the final study grades. Two versions of the software were used: one that
detected microaneurysms alone, and one that detected blot haemorrhages and exudates in addition to microaneurysms.
Results for each version were calculated once using both fields and once using the macula-centred field alone.
Results: Of the 8,271 episodes, 346 (4.2%) were considered unassessable. Referable disease was detected in 587 episodes
(7.1%). The sensitivity of the automated system for detecting unassessable images ranged from 97.4% to 99.1% depending
on configuration. The sensitivity of the automated system for referable episodes ranged from 98.3% to 99.3%. All the
episodes that included proliferative or pre-proliferative retinopathy were detected by the automated system regardless of
con
显示全部