Reflectance and Thickness Analysis of Retinal Layers in Patients with Epiretinal Membranes Using Spectral-Domain OCT before and after Vitrectomy with Membrane Peeling

Analysing early changes of photoreceptor layer thickness following surgery in eyes with epiretinal membranes

BACKGROUND/OBJECTIVES

To analyse short-term changes of mean photoreceptor thickness (PRT) on the ETDRS-grid after vitrectomy and membrane peeling in patients with epiretinal membrane (ERM).

SUBJECTS/METHODS

Forty-eight patients with idiopathic ERM were included in this prospective study. Study examinations comprised best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) before surgery, 1 week (W1), 1 month (M1) and 3 months (M3) after surgery. Mean PRT was assessed using an automated algorithm and correlated with BCVA and central retinal thickness (CRT).

RESULTS

Regarding PRT changes of the study eye in comparison to baseline values, a significant decrease at W1 in the 1 mm, 3 mm and 6 mm area (all p-values < 0.001), at M1 (p = 0.009) and M3 (p = 0.019) in the central 1 mm area, a significant increase at M3 in the 6 mm area (p < 0.001), but no significant change at M1 in the 3 mm and 6 mm area and M3 in the 3 mm area (all p-values > 0.05) were observed. BCVA increased significantly from baseline to M3 (0.3LogMAR-0.15LogMAR, Snellen equivalent = 20/40-20/28 respectively; p < 0.001). There was no correlation between baseline PRT and BCVA at any visit after surgery, nor between PRT and BCVA at any visit (all p-values > 0.05). Decrease in PRT in the 1 mm (p < 0.001), 3 mm (p = 0.013) and 6 mm (p = 0.034) area after one week correlated with the increase in CRT (449.9 µm-462.2 µm).

CONCLUSIONS

Although the photoreceptor layer is morphologically affected by ERMs and after their surgical removal, it is not correlated to BCVA. Thus, patients with photoreceptor layer alterations due to ERM may still benefit from surgery and achieve good functional rehabilitation thereafter.

Double peak axial length measurement signal in cataract patients with epiretinal membrane

PURPOSE

To evaluate the accuracy of axial length (AL) measurement for intraocular lens (IOL) calculation in patients with cataract and epiretinal membrane (ERM).

METHODS

This prospective, cross-sectional study was performed in cataract patients with ERM. All subjects were sent for standard optical biometry, prepared for cataract surgery. Signals of AL measurement were detected as double peaks and recorded as AL1 (first peak), and AL2 (second peak). The IOL power was calculated from AL1 and AL2, and reported as IOL1 and IOL2. The IOL2 was chosen for cataract surgery in all cases. Postoperative predictive errors were compared between IOL1 and IOL2.

RESULTS

Thirty-seven eyes from 37 patients were included. Mean AL1 was significantly shorter than AL2 (23.13 ± 1.28 vs. 23.60 ± 1.34 mm, p < 0.001), resulting in higher power of IOL1 than IOL2 (mean difference was 1.53 ± 0.96 diopters, p < 0.001). At 3-months post-operation, twenty-nine eyes (78.4%) (95% CI 62.8%-88.6%) showed refractive error within ± 0.5 diopter and all eyes were within ± 1.0 diopter. Postoperative predictive errors including mean arithmetic error (ME) and mean absolute error (MAE) of IOL2 were significantly lower than those of IOL1 (ME: IOL1 vs. IOL2, -0.94 ± 0.91 vs. 0.08 ± 0.51; MAE: 0.97 ± 0.88 vs. 0.39 ± 0.33 diopter, all p < 0.001).

CONCLUSIONS

AL measurement in ERM can be detected as a double peak signal during biometric measurement. The IOL power calculated from the first and second peak signals is significantly different. However, the IOL power derived from the second peak signal provides better refractive outcomes. The results suggest that the second peak signal represents an accurate AL measurement.

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning

Purpose

Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal ganglion cells (RGCs) are known to be involved in contrast coding, it still remains unknown whether the retinal layers containing RGCs are linked to a person's contrast sensitivity (e.g., Pelli-Robson contrast sensitivity) and, if so, to what extent the retinal layers are related to behavioral contrast sensitivity. Thus the current study aims to identify the retinal layers and features critical for predicting a person's contrast sensitivity via deep learning.

Methods

Data were collected from 225 subjects including individuals with either glaucoma, age-related macular degeneration, or normal vision. A deep convolutional neural network trained to predict a person's Pelli-Robson contrast sensitivity from structural retinal images measured with optical coherence tomography was used. Then, activation maps that represent the critical features learned by the network for the output prediction were computed.

Results

The thickness of both ganglion cell and inner plexiform layers, reflecting RGC counts, were found to be significantly correlated with contrast sensitivity (r = 0.26 ∼ 0.58, Ps < 0.001 for different eccentricities). Importantly, the results showed that retinal layers containing RGCs were the critical features the network uses to predict a person's contrast sensitivity (an average R2 = 0.36 ± 0.10).

Conclusions

The findings confirmed the structure and function relationship for contrast sensitivity while highlighting the role of RGC density for human contrast sensitivity.

Swept-source OCT reduces the risk of axial length measurement errors in eyes with cataract and epiretinal membranes

AIMS

To compare the biometric data from partial coherence interferometry (PCI) and swept-source OCT (SS-OCT) in patients with age-related cataract and epiretinal membrane (ERM): ERM, ERM with foveoschisis and macular pseudohole.

METHODS

49 eyes of 49 subjects including 36 ERM, 9 ERM foveoschisis and 4 macular pseudohole were analysed to evaluate the axial length (AL) measurements and the presence of AL measurement errors, defined basing on the shape of the biometric output graphs and on the concordance of AL values between instruments. Eyes with ERM were divided in four stages according to OCT features (i.e. presence/absence of the foveal pit, presence of ectopic inner foveal layers, disrupted retinal layers).

RESULTS

The devices provided similar mean AL measurements in all subgroups, with differences <0.1 mm in 41/49 cases (83.6%). AL measurement errors were observed in ERM stages 3 and 4, characterized by ectopic inner foveal layers, and were significantly more frequent with the PCI (8/17, 47%) as compared with the SS-OCT device (2/17, 12%), p = 0.02. The refractive prediction error in cases with AL measurement errors was significantly greater using the PCI compared to the SS-OCT device (p<0.05).

CONCLUSION

Both devices provide reliable biometric data in the majority of patients and can be used in the preoperative assessment of patients with age-related cataract and ERM. In eyes with ectopic inner foveal layers, attention should be paid as AL measurement and refractive prediction errors may occur, more frequently with the PCI device.