Diabetic retinopathy: A review on its pathophysiology and novel treatment modalities

Diabetes mellitus (DM) is a chronic metabolic non-communicable disease with the ability to cause serious microvascular and macrovascular complications throughout the body, including in the eye. Diabetic retinopathy (DR), present in one-third of patients with diabetes, is a vision-threatening complication caused by uncontrolled diabetes, which greatly affects the retinal blood vessels and the light-sensitive inner retina, eventually leading to blindness. Several epidemiological studies elucidate that DR can vary by age of onset, duration, types of diabetes, and ethnicity. Recent studies show that the pathogenesis of diabetic retinopathy has spread its roots beyond merely being the result of hyperglycemia. The complexity of its etiopathology and diagnosis makes therapeutic intervention challenging. This review throws light on the pathological processes behind DR, the cascade of events that follow it, as well as the available and emerging treatment options.

Intravitreal therapy for the management of diabetic retinopathy: A concise review

Diabetic retinopathy (DR) is a serious microvascular complication of diabetes mellitus and may result in irreversible visual loss. Laser treatment has been the gold standard treatment for diabetic macular edema and proliferative diabetic retinopathy for many years. Of late, intravitreal therapy has emerged as a cornerstone in the management of DR. Among the diverse pharmacotherapeutic options, anti-vascular endothelial growth factor agents have demonstrated remarkable efficacy by attenuating neovascularization and reducing macular edema, thus preserving visual acuity in DR patients.

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.

Phacoemulsification Techniques and Their Effects on Corneal Endothelial Cells and Visual Acuity: A Review of "Direct-Chop" and "Stop-and-Chop" Approaches Under Topical Anesthesia

Phacoemulsification is a widely adopted technique in cataract surgery that offers a minimally invasive approach to lens removal and intraocular lens implantation. Among the various methods of phacoemulsification, "direct-chop" and "stop-and-chop" techniques are particularly notable for their efficiency and safety profiles. This review aims to evaluate the effects of these two techniques on corneal endothelial cells and visual acuity, specifically under topical anesthesia. Cataract surgery outcomes hinge on the preservation of corneal endothelial cells and the achievement of optimal visual acuity. Endothelial cell loss can lead to corneal decompensation, while visual acuity is a primary measure of surgical success. The "direct-chop" technique involves the immediate chopping of the lens nucleus after groove creation, reducing phacoemulsification time and energy. Conversely, the "stop-and-chop" technique incorporates a central groove before chopping, offering increased control and safety. This review synthesizes current research and clinical studies to compare these techniques, focusing on their respective impacts on endothelial cell count and postoperative visual acuity. It examines the advantages and disadvantages of each approach, considers the role of surgeon experience, phacoemulsification energy, and anterior chamber stability, and assesses patient outcomes under topical anesthesia. The findings aim to provide insights that can guide surgeons in selecting the most appropriate technique for their patients, ultimately enhancing surgical outcomes by ensuring the preservation of corneal health and the achievement of superior visual acuity.

A randomized controlled trial to assess safety and efficacy between terminal chop, stop and chop, and direct chop

PURPOSE

The goal in developing new techniques of cataract surgery is to provide a safer, more efficient surgical experience with the lowest complication rate and endothelial cell loss. We compared the efficiency and safety of stop-and-chop, direct chop, and the novel terminal chop techniques of nuclear fragmentation for cataracts grade II-V.

METHODS

We conducted a prospective randomized clinical trial comparing three different techniques of phacoemulsification, namely, stop-and-chop, direct chop, and terminal chop to assess any differences between them and to establish whether any one method was superior to the others. The pre- and postoperative parameters studied, included central corneal thickness (CCT), ultrasonic time (UST), endothelial cell density (CD), cell loss and effective phacoemulsification time (EPT), average cumulative dissipative energy (CDE), and best-corrected visual acuity, among others.

RESULTS

307 eyes were recruited to the study, 102 were recruited to the stop-and-chop group, 103 to the direct chop group, and 102 to the terminal chop group. Statistical differences were found between the techniques with regard to postoperative CCT among NS II (P. 0001) and NS IV cataracts (P = .005) with the lowest values in the terminal chop group among NS II, NS III, and NS IV cataracts. Endothelial cell loss was minimum with a terminal chop in NS II (P = .018) and NS IV cataracts (P = .245). CDE was minimum in terminal chop across different cataract densities.

CONCLUSION

Terminal chop showed improvement over the other two techniques in terms of CDE and was comparable to them with regard to other parameters.

Melatonin alleviates renal injury in diabetic rats by regulating autophagy

Melatonin (MLT) is a biologically active indoleamine involved in regulating various biological rhythms, which is deficient in individuals with Type 2 diabetes. The present study examined the effects of MLT on diabetic neuropathy (DN). Diabetic rats received MLT treatment for 12 weeks, after which changes in kidney histology, oxidative damage, mitochondrial morphology and autophagy were measured. The glucose tolerance‑ and isoflurane tolerance‑area under the curve (AUC) values and the relative renal weight index (RI) in the diabetes mellitus (DM) group of rats were significantly higher compared with those in the control group. A significant increase in malondialdehyde (MDA) content, and decreases in the activity of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH‑Px) and GSH were demonstrated in the kidneys of DM rats compared with those in the control rats. Histological staining of DM rat kidney tissue with hematoxylin and eosin, Masson's trichome and Periodic acid‑Schiff demonstrated glomerular and tubule lesions, and an increase in collagen compared with control rats. Protein expression levels of LC3II, P62, collagen IV (COL‑IV) and α‑SMA were increased in DM rats and HG‑induced NRK‑52E cells compared with those in the control groups. Phosphorylation of AMPK was reduced, whereas phosphorylation of PI3K, Akt and mTOR were increased in vivo and in vitro. Notably, MLT treatment significantly reduced glucose tolerance‑AUC and RI, decreased MDA content, and increased SOD, CAT, GSH‑Px and GSH activity. Glomerular and tubule lesions improved, collagen was decreased and mitochondrial damage was alleviated by MLT treatment. MLT treatment also decreased the protein expression levels of LC3II, P62 and COL‑IV, whereas the phosphorylation of AMPK was significantly increased, which inhibited the phosphorylation of PI3K, AKT and mTOR in vivo and in vitro. These results demonstrated that MLT protects against DN and NRK‑52E cell injury through inhibiting oxidative damage and regulating autophagy via the PI3K/AKT/mTOR signaling pathway.

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

Everything real about unreal artificial intelligence in diabetic retinopathy and in ocular pathologies

Artificial Intelligence is a multidisciplinary field with the aim of building platforms that can make machines act, perceive, reason intelligently and whose goal is to automate activities that presently require human intelligence. From the cornea to the retina, artificial intelligence (AI) is expected to help ophthalmologists diagnose and treat ocular diseases. In ophthalmology, computerized analytics are being viewed as efficient and more objective ways to interpret the series of images and come to a conclusion. AI can be used to diagnose and grade diabetic retinopathy, glaucoma, age-related macular degeneration, cataracts, IOL power calculation, retinopathy of prematurity and keratoconus. This review article intends to discuss various aspects of artificial intelligence in ophthalmology.

Active Learning Performance in Labeling Radiology Images Is 90% Effective

To train artificial intelligence (AI) systems on radiology images, an image labeling step is necessary. Labeling for radiology images usually involves a human radiologist manually drawing a (polygonal) shape onto the image and attaching a word to it. As datasets are typically large, this task is repetitive, time-consuming, error-prone, and expensive. The AI methodology of active learning (AL) can assist human labelers by continuously sorting the unlabeled images in order of information gain and thus getting the labeler always to label the most informative image next. We find that after about 10%, depending on the dataset, of the images in a realistic dataset are labeled, virtually all the information content has been learnt and the remaining images can be automatically labeled. These images can then be checked by the radiologist, which is far easier and faster to do. In this way, the entire dataset is labeled with much less human effort. We introduce AL in detail and expose the effectiveness using three real-life datasets. We contribute five distinct elements to the standard AL workflow creating an advanced methodology.