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Purpose: To evaluate the performance of an automated choroid segmentationalgorithm in optical coherence tomography (OCT) data using a longitudinalkidney donor and recipient cohort. Methods: We assessed 22 donors and 23patients requiring renal transplantation over up to 1 year post-transplant. Wemeasured choroidal thickness (CT) and area and compared our automated CTmeasurements to manual ones at the same locations. We estimated associationsbetween choroidal measurements and markers of renal function (estimatedglomerular filtration rate (eGFR), serum creatinine and urea) using correlationand linear mixed-effects (LME) modelling. Results: There was good agreementbetween manual and automated CT. Automated measures were more precise becauseof smaller measurement error over time. External adjudication of majordiscrepancies were in favour of automated measures. Significant differenceswere observed in the choroid pre- and post-transplant in both cohorts, and LMEmodelling revealed significant linear associations observed between choroidalmeasures and renal function in recipients. Significant associations were mostlystronger with automated CT (eGFR P<0.001, creatinine P=0.004, urea P=0.04)compared to manual CT (eGFR P=0.002, creatinine P=0.01, urea P=0.03).Conclusions: Our automated approach has greater precision than human-performedmanual measurements, which may explain stronger associations with renalfunction compared to manual measurements. To improve detection of meaningfulassociations with clinical endpoints in longitudinal studies of OCT, reducingmeasurement error should be a priority, and automated measurements help achievethis. Translational relevance: We introduce a novel choroid segmentationalgorithm which can replace manual grading for studying the choroid in renaldisease, and other clinical conditions.

Evaluation of an automated choroid segmentation algorithm in a longitudinal kidney donor and recipient cohort