Automated Quantification of Choriocapillaris Lesion Area in Patients With Posterior Uveitis

PURPOSE: 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. DESIGN: Observational case series. METHODS: Twenty eyes of 14 patients with posterior uveitis were imaged. The machine-generated en face OCTA CC slabs were exported to a computing platform, where a custom algorithm performed unsupervised lesion boundary delineation and area quantification. Lesions identified by the algorithm (AG) were compared to those identified by 2 masked human graders (HG1 and HG2), using the Sorensen-Dice coefficient (DSC) and intraclass correlation coefficient (ICC). Intragrader and intravisit reliability were determined by coefficient of variation (CV) and DSC. RESULTS: 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 of 20 (70%) eyes and at least 1 HG in 16 of 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. Intravisit repeatability for the AG was superior to both HGs (CV 2.6% vs > 5%). CONCLUSION: This custom algorithm demonstrated a high degree of agreement with HGs in identification of inflammatory CC lesions and outperformed HGs in reproducibility. Automated CC lesion delineation will support the development of objective and quantitative biomarker of disease activity in patients with posterior uveitis. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study validated a custom algorithm for automated identification and quantification of clinically relevant inflammatory choriocapillaris lesions. The algorithm demonstrated good agreement with human graders and outperformed them in reproducibility. Automated lesion delineation could support the development of objective and quantitative biomarkers for disease activity in patients with posterior uveitis.