Applying an Open-Source Segmentation Algorithm to Different OCT Devices in Multiple Sclerosis Patients and Healthy...

Area of Science:

• Ophthalmology
• Neuroscience
• Medical Imaging

Background:

• Standardized retinal layer thickness measurements are crucial for multiple sclerosis (MS) clinical trials.
• Current optical coherence tomography (OCT) segmentation methods lack cross-platform compatibility, limiting data integration.
• Developing a universal segmentation algorithm is essential for advancing MS research.

Purpose of the Study:

• To assess the agreement of retinal layer thicknesses derived from an open-source, automated segmentation algorithm across two OCT devices.
• To evaluate both cross-sectional and longitudinal agreement of these measurements.
• To determine the algorithm's utility in combining data from different OCT platforms for MS trials.

Main Methods:

• Macular scans from Cirrus HD-OCT and Spectralis OCT devices were analyzed using an open-source, automated segmentation algorithm.
• A cohort of 68 MS patients and 22 healthy controls were included for cross-sectional analysis.
• A longitudinal cohort of 51 subjects with a mean follow-up of 1.4 years was examined for changes over time.

Main Results:

• The algorithm demonstrated low mean differences and narrow limits of agreement for most retinal layer thicknesses cross-sectionally.
• Longitudinal analysis revealed low mean differences in layer thickness changes, although with wider limits of agreement.
• Comparisons of the rate of change in retinal layer thicknesses over time showed consistent results between the OCT platforms.

Conclusions:

• Retinal layer thickness measurements exhibit good cross-sectional and longitudinal agreement between the two OCT scanners at the cohort level.
• The open-source algorithm facilitates combining OCT data from disparate platforms, enhancing its use in MS clinical trials.
• While cohort-level agreement is strong, individual-level variability in measurements should be considered.