Automated characterization of pigment epithelial detachment by optical coherence tomography.

PURPOSE

To assess the accuracy of automated classification of pigment epithelial detachments (PED) by using a software algorithm applied to spectral-domain optical coherence tomography (SD-OCT) scans.

METHODS

HD-OCT (Cirrus; Carl Zeiss Meditec, Dublin, CA) volume scans (512 × 128) were retrospectively collected from 46 eyes of 33 patients with evidence of PED in the setting of age-related macular degeneration (AMD, n = 28) or central serous chorioretinopathy (CSCR, n = 5). In these eyes, 168 PEDs were automatically detected with a system-associated tool (Cirrus HD-OCT RPE Elevation Analysis; Carl Zeiss Meditec). Two independent, certified Doheny Image Reading Center (DIRC) OCT graders classified these PEDs into three categories--serous, drusenoid, or fibrovascular--via inspection of the B-scans. Manual classification results served as the gold standard for comparisons with automated classification. For automated classification, interindividual variation in intensities was normalized in all images. Individual A-scans within the detected PEDs were then automatically classified into one of three categories based on the mean internal intensity and the standard deviation of the internal intensity: mean intensity <30 (serous type); mean intensity ≥30 but <60 or mean intensity ≥30 and SD ≥30 (fibrovascular type); or mean intensity ≥60 and SD < 30 (drusenoid type). Individual PEDs were then automatically classified into the same three categories based on the predominant type of A-scan within the PED. For mixed PEDs (many A-scans of each type), a risk index for neovascularization was computed based on the percentage of fibrovascular A-scans. In addition, a confidence index was computed for each PED based on its mathematical distance from the PED category boundaries.

RESULTS

Among the 168 PEDs, the DIRC graders classified 16 as serous, 88 as fibrovascular, and 64 as drusenoid PEDs. The automated algorithm classified 14 as serous, 96 as fibrovascular, and 58 as drusenoid PEDs. The sensitivity and specificity values for automated classification according to type of PED were 88% and 100% for serous, 76% and 64% for fibrovascular, and 58% and 81% for drusenoid, respectively.

CONCLUSIONS

Automated classification of PEDs using internal reflectivity characteristics appears to be sensitive for detecting serous and fibrovascular PEDs. Automated classification and quantification of PEDs may be a useful tool in future studies for stratifying PEDs according to risk and possibly predicting the risk of advanced AMD.