Automated Quantification of Choriocapillaris Lesion Area in Patients with Posterior Uveitis.
To validate a custom algorithm for automated identification and quantification of clinically relevant inflammatory choriocapillaris (CC) lesions from en face swept source optical coherence tomography (SS-OCTA) images.observational case series METHODS: : Twenty eyes of 14 patients with posterior uveitis were imaged using the PLEX® Elite 9000. The machine-generated en face OCTA CC slabs were exported to MATLAB where a custom algorithm performed unsupervised lesion boundary delineation and area quantification. Lesions identified by the algorithm (AG) were compared to those identified by two masked human graders (HG1 and HG2), using the Sørensen-Dice coefficient (DSC) and intraclass correlation coefficient (ICC). Intra-grader and intra-visit reliability were determined by coefficient of variation (CV) and DSC.The AG demonstrated excellent agreement with both HGs in determination of lesion area (HG1 vs. AG ICC 0.92, 95% CI 0.81-0.97, HG2 vs. AG ICC 0.91, 95% CI 0.78-0.97). The AG demonstrated good spatial overlap (DSC≥0.70) with both HGs in 14/20 (70%) eyes and at least one HG in 16/20 (80%) eyes. Poor spatial overlap (DSC between 0.31 and 0.69) was associated with the presence of a choroidal neovascular membrane and low contrast lesion boundaries. Intra-visit repeatability for the AG was superior to both HGs (CV 2.6% vs >5%).This custom algorithm demonstrated a high degree of agreement with human graders in identification of inflammatory CC lesions, and outperformed human graders in reproducibility. Automated CC lesion delineation will support the development of objective and quantitative biomarker of disease activity in patients with posterior uveitis.
Authors: K Matthew McKay, Zhongdi Chu, Joon-Bom Kim, Alex Legocki, Xiao Zhou, Meng Tian, Marion R Munk, Ruikang K Wang, Kathryn L Pepple