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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Aged, Cross-Sectional Studies, Female, Glaucoma, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Nerve Fibers, Tomography, Optical Coherence.
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Does signal normalization reduce systematic measurement differences between spectral-domain optical coherence tomography devices? Context: To test the effect of a novel signal normalization method for reducing systematic optical coherence tomography (OCT) measurement differences among multiple spectral-domain (SD) OCT devices. A total of 109 eyes from 59 subjects were scanned with two SD-OCT devices (Cirrus and RTVue) at the same visit. Optical coherence tomography image data were normalized to match their signal characteristics between the devices. To compensate signal strength differences, custom high dynamic range (HDR) processing was also applied only to images with substantially lower signal strength. Global mean peripapillary retinal nerve fiber layer (RNFL) thicknesses were then measured automatically from all images using custom segmentation software and were compared to the original device outputs. Structural equation models were used to analyze the absolute RNFL thickness difference between original device outputs and our software outputs after signal normalization. The device-measured RNFL thickness showed a statistically significant difference between the two devices (mean absolute difference 10.58 μm, P < 0.05), while there was no significant difference after normalization on eyes with 62.4-μm or thicker RNFL (mean absolute difference 2.95 μm, P < 0.05).
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The signal normalization method successfully reduces the systematic difference in RNFL thickness measurements between two SD-OCT devices. Enabling direct comparison of RNFL thickness obtained from multiple devices would broaden the use of OCT technology in both clinical and research applications. Final Decision: yes