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Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
Published on: January 10, 2019
A systematic evaluation of human expert agreement on optical coherence tomography biomarkers using multiple devices
Martin Michl1, Martina Neschi2, Alexandra Kaider3
1Department of Ophthalmology, Vienna Reading Center, Medical University of Vienna, Vienna, Austria.
Objectives:
To assess the agreement in evaluating optical coherence tomography (OCT) variables in the leading macular diseases such as neovascular age-related macular degeneration (nAMD), diabetic macular oedema (DMO) and retinal vein occlusion (RVO) among OCT-certified graders.
Methods:
SD-OCT volume scans of 356 eyes were graded by seven graders. The grading included presence of intra- and subretinal fluid (IRF, SRF), pigment epithelial detachment (PED), epiretinal membrane (ERM), conditions of the vitreomacular interface (VMI), central retinal thickness (CRT) at the foveal centre-point (CP) and central millimetre (CMM), as well as height and location of IRF/SRF/PED. Kappa statistics (κ) and intraclass correlation coefficient (ICC) were used to report categorical grading and measurement agreement.
Results:
The overall agreement on the presence of IRF/SRF/PED was κ = 0.82/0.85/0.81; κ of VMI condition was 0.77, that of ERM presence 0.37. ICC for CRT measurements at CP and CMM was excellent with an ICC of 1.00. Height measurements of IRF/SRF/PED showed robust consistency with ICC = 0.85-0.93. There was substantial to almost perfect agreement in locating IRF/SRF/PED with κ = 0.67-0.86. Between diseases, κ of IRF/SRF presence was 0.69/0.80 for nAMD, 0.64/0.83 for DMO and 0.86/0.89 for RVO.
Conclusion:
Even in the optimized setting, featuring certified graders, standardized image acquisition and the use of a professional reading platform, there is a disease dependent variability in biomarker evaluation that is most pronounced for IRF in nAMD as well as DMO. Our findings highlight the variability in the performance of human expert OCT grading and the need for AI-based automated feature analyses.
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