  Purpose: To evaluate the performance of an automated choroid segmentation
algorithm in optical coherence tomography (OCT) data using a longitudinal
kidney donor and recipient cohort. Methods: We assessed 22 donors and 23
patients requiring renal transplantation over up to 1 year post-transplant. We
measured choroidal thickness (CT) and area and compared our automated CT
measurements to manual ones at the same locations. We estimated associations
between choroidal measurements and markers of renal function (estimated
glomerular filtration rate (eGFR), serum creatinine and urea) using correlation
and linear mixed-effects (LME) modelling. Results: There was good agreement
between manual and automated CT. Automated measures were more precise because
of smaller measurement error over time. External adjudication of major
discrepancies were in favour of automated measures. Significant differences
were observed in the choroid pre- and post-transplant in both cohorts, and LME
modelling revealed significant linear associations observed between choroidal
measures and renal function in recipients. Significant associations were mostly
stronger 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-performed
manual measurements, which may explain stronger associations with renal
function compared to manual measurements. To improve detection of meaningful
associations with clinical endpoints in longitudinal studies of OCT, reducing
measurement error should be a priority, and automated measurements help achieve
this. Translational relevance: We introduce a novel choroid segmentation
algorithm which can replace manual grading for studying the choroid in renal
disease, and other clinical conditions.
