Relationship between Full-Thickness Macular Hole Onset and Posterior Vitreous Detachment: A Temporal Onset Theory

Macular hole with retinal pigment epithelium tear after anti-VEGF therapy in an eye with neovascular age-related macular degeneration

Purpose

To report a case of a full-thickness macular hole (FTMH) associated with a retinal pigment epithelium (RPE) tear after anti-vascular endothelial growth factor (anti-VEGF) therapy for neovascular age-related macular degeneration (nvAMD), which was successfully closed by vitreous surgery.

Observations

A 73-year-old man with nvAMD in the right eye received an intravitreal aflibercept injection due to enlarged pigment epithelial detachment. However, 2 days after the third injection, the patient experienced a sudden decline in vision. An FTMH with a tear in the underlying RPE was detected. The FTMH was closed using vitrectomy combined with the inverted internal limiting membrane (ILM) flap technique.

Conclusions and Importance

Our case highlights a rare complication of both an FTMH and an RPE tear after anti-VEGF therapy. Vitrectomy, with the inverted ILM flap technique, proved effective in closing the FTMH despite the complexity of the case.

A Case of Closure of Recurrent Full-Thickness Macular Hole by Spontaneous Retinal Detachment around the Macular Hole and Gas Tamponade

Introduction

Here, we present a case of full-thickness macular hole (FTMH) recurrence following two vitrectomies, accompanied by additional internal limiting membrane (ILM) peeling and gas tamponade. Ultimately, FTMH closure was accomplished by spontaneous retinal detachment around the macular hole and gas tamponade alone.

Case Presentation

The patient, a 54-year-old woman with a lamellar macular hole, had a visual acuity of 20/100 in her left eye. The treatment regimen included cataract surgery, a 25-gauge pars plana vitrectomy involving ILM peeling, application of the lamellar hole epiretinal proliferation embedding technique, and subsequent gas tamponade. Closure of the lamellar macular hole was observed a month post-surgery, improving visual acuity to 20/40. However, FTMH developed 3 months after the initial surgery, resulting in visual acuity decline to 20/100. A 25-gauge pars plana vitrectomy was performed with extensive ILM peeling and 20% sulfur hexafluoride gas tamponade. FTMH closure was noted within 19 days after reoperation, enhancing visual acuity to 20/66. Approximately 1.5 months after reoperation, a pinhole-shaped macular hole was identified, and the patient opted for follow-up observation due to her refusal to undergo additional surgery. As the macular hole gradually enlarged resembling retinal detachment, outpatient fluid-gas exchange with 14% perfluoropropane was performed 3.5 months after reoperation. The FTMH closed within a week post-gas injection and remained closed for more than 1 year. Consequently, visual acuity in the left eye was sustained at 20/50.

Conclusion

We encountered a case that might highlight the significance of releasing subretinal adhesions surrounding a FTMH for successful closure.

Wavelet scattering transform application in classification of retinal abnormalities using OCT images

To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were [Formula: see text] and [Formula: see text], respectively. We achieved an accuracy of [Formula: see text] in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of [Formula: see text] and [Formula: see text], respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity.