Can automated analysis of sequential retinal images of people attending diabetic retinopathy screening predict future referral to ophthalmology?. (September 2016)

• Record Type: Journal Article
• Authors: Philip, Sam; Ahmed, Arfan; Prescott, Gordon; Staff, Roger; Sharp, Peter; Xu, Jiawei; Olson, John
• Is Part Of: Physica medica. Volume 32(2016)Supplement 3
• Page Start: 275
• Publication Date: 2016-09
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2016.07.607
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : Introduction: Providing systemic screening to a growing diabetes population is a challenge for most screening programmes. We have previously developed software for the automated detection of diabetic retinopathy which is now routinely used within the Scottish Diabetic Retinopathy Screening programme. Purpose: In this study we explored whether automated analysis of sequential retinal images of people attending screening can predict future referral to ophthalmology. Materials and methods: We developed software to automatically measure whether the same microaneurysm (MA), the main indicator of retinopathy, appeared in sequential images and whether new MAs appeared, how close the MAs were to the fovea, the number of MAs within each quadrant of the retina, and the presence of other indicators of retinopathy, namely exudates and haemorrhages. A retrospective cohort study was conducted using 12, 754 subjects to assess whether these features predicted retinopathy development. Results: A number of the novel features were independently associated with retinopathy progression of retinal images. These were higher MA counts close to the fovea, higher MA turnover between screening episodes and higher probabilities of haemorrhages anywhere in the image and exudates close to the fovea. We developed a model to estimate the risk of progression over the next 15 months. The sensitivity, specificity, PPV and NPV were 83.6%, 79.5%, 8.5% and 99.5%, respectively. We also developed a model for the risk of progression over the next 5 years. Conclusion: Features of retinopathy derived from automated analysis of sequential photographs can help predict the risk of progression to retinopathy needing referral to ophthalmology.