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Automated Measurement of Microaneurysm Turnover

¹From the Departments of Bio-Medical Physics and Bio-Engineering and ³Ophthalmology, University of Aberdeen, Foresterhill, Aberdeen, United Kingdom; and the ²Eye Clinic, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, United Kingdom.

PURPOSE. An automated system for the measurement of microaneurysm (MA) turnover was developed and compared with manual measurement. The system analyses serial fluorescein angiogram (FA) or red-free (RF) fundus images; fluorescein angiography was used in this study because it is the more sensitive test for MAs. Previous studies have shown that the absolute number of MAs observed does not reflect the dynamic temporal nature of the MA population. In this study, almost half of the MAs present at baseline had regressed after a year and been replaced by new lesions elsewhere.

METHODS. Two clinical datasets were used to evaluate the performance of the automated turnover measurement system. The first consisted of 10 patients who had two fluorescein angiograms acquired a year apart. These data were analyzed, both manually and using the automated system, to investigate the inter- and intraobserver variations associated with manual measurement and to assess the performance of the automated system. The second dataset contained FAs from a further 25 patients. This dataset was analyzed only with the automated system to investigate some properties of microaneurysm turnover, in particular the differing detection sensitivities of new, static and regressed microaneurysms.

RESULTS. Manual measurements exhibited large inter- and intraobserver variation. The sensitivity and specificity of the automated system were similar to those of the human observers. However, the automated measurements were more consistent—an important condition for accurate turnover quantification. Regressed MAs were more difficult to detect reliably than new MAs, which were themselves more difficult to detect reliably than static MAs.

CONCLUSIONS. The automated system was shown to be fast, reliable, and repeatable, making it suitable for processing large numbers of images. Performance was similar to that of trained manual observers.