Internal limiting membrane peeling is now widely used in the treatment of vitreoretinal diseases, such as idiopathic macular hole, epiretinal membrane, macular edema, traumatic retinopathy, retinoschisis, and optic pit, especially macular diseases. Due to the attention paid to the physiological function of the internal limiting membrane, there is still controversies about whether the internal limiting membrane is removed, and the area and the way of the removal in vitrectomy of the above diseases. Major complications have been reported in literature: effects on internal retinal structure, retinal and choroidal blood flow, retinal electrical activity, potential retinal toxicity of stain, changes in the anatomy of macular area, changes in visual field and potential damage to vision. In this paper, we reviewes the complications of internal limiting membrane peeling in the treatment of macular hole and epiretinal macular membrane.
Optical coherence tomography (OCT) is a non-invasive, rapid optical medical imaging modality and has become a hot topic in biomedical research. In recent years, several functional OCTs have emerged, including Doppler OCT, polarization-sensitive OCT, spectroscopic OCT, and optical coherence tomographic elastography, etc. These newer advances in functional OCT broaden the potential clinical application of OCT by providing novel ways to observe and understand tissue activity that cannot be accomplished by other current imaging methodologies.
With the rapid development of artificial intelligence (AI), especially deep learning, AI research in the field of ophthalmology has presented a trend of diversification in disease types, generalization in scenarios and deepening in researches. The AI algorithm has showed a good performance in the studies of diabetic retinopathy, age-related macular degeneration, glaucoma and other ocular diseases, yielding up the great potential of ophthalmic AI. However, most studies are still in their infancy, and the application of ophthalmic AI still faces many challenges such as lack of interpretability for results, deficiency of data standardization, and insufficiency of clinical applicability. At the same time, it should also be noted that the development of multi-modal imaging, the innovation of digital technologies (such as 5G and the Internet of Things) and telemedicine, and the new discovery that retina status can reflect systemic diseases have brought new opportunities for the development of ophthalmic AI. Learn the current status of AI research in the field of ophthalmology, grasp the new challenges and opportunities in its development process, successfully realizing the transformation of ophthalmic AI from research to practical application.