Diabetes more than retinopathy, it’s effect on the anterior segment of eye

Diabetes mellitus (DM) is one of the chronic metabolic noncommunicable diseases that has attained worldwide epidemics. It threatens healthy life around the globe, with mild-to-severe secondary complications and leads to significant illness including nephropathy, neuropathy, retinopathy, and macrovascular abnormalities including peripheral vasculopathy, and ischaemic heart disease. Research into diabetic retinopathy (DR), which affects one-third of persons with diabetes, has made considerable strides in recent years. In addition, it can lead to several anterior segment complications such as glaucoma, cataract, cornea, conjunctiva, lacrimal glands and other ocular surface diseases. Uncontrolled DM also caused gradual damage to corneal nerves and epithelial cells, which raises the likelihood of anterior segment diseases including corneal ulcers, dry eye disease, and chronic epithelial abnormalities. Although DR and other associated ocular complications are well-known, the complexity of its aetiology and diagnosis makes therapeutic intervention challenging. Strict glycaemic control, early detection and regular screening, and meticulous management is the key to halting the progression of the disease. In this review manuscript, we aim to provide an in-depth understanding of the broad spectrum of diabetic complications in the anterior segment of the ocular tissues and illustrate the progression of diabetes and its pathophysiology, epidemiology, and prospective therapeutic targets. This first such review article will highlight the role of diagnosing and treating patients with a plethora of anterior segment diseases associated with diabetes, which are often neglected.

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.

Evaluating the Viability of a Smartphone-Based Annotation Tool for Faster and Accurate Image Labelling for Artificial Intelligence in Diabetic Retinopathy

Introduction

Deep Learning (DL) and Artificial Intelligence (AI) have become widespread due to the advanced technologies and availability of digital data. Supervised learning algorithms have shown human-level performance or even better and are better feature extractor-quantifier than unsupervised learning algorithms. To get huge dataset with good quality control, there is a need of an annotation tool with a customizable feature set. This paper evaluates the viability of having an in house annotation tool which works on a smartphone and can be used in a healthcare setting.

Methods

We developed a smartphone-based grading system to help researchers in grading multiple retinal fundi. The process consisted of designing the flow of user interface (UI) keeping in view feedback from experts. Quantitative and qualitative analysis of change in speed of a grader over time and feature usage statistics was done. The dataset size was approximately 16,000 images with adjudicated labels by a minimum of 2 doctors. Results for an AI model trained on the images graded using this tool and its validation over some public datasets were prepared.

Results

We created a DL model and analysed its performance for a binary referrable DR Classification task, whether a retinal image has Referrable DR or not. A total of 32 doctors used the tool for minimum of 20 images each. Data analytics suggested significant portability and flexibility of the tool. Grader variability for images was in favour of agreement on images annotated. Number of images used to assess agreement is 550. Mean of 75.9% was seen in agreement.

Conclusion

Our aim was to make Annotation of Medical imaging easier and to minimize time taken for annotations without quality degradation. The user feedback and feature usage statistics confirm our hypotheses of incorporation of brightness and contrast variations, green channels and zooming add-ons in correlation to certain disease types. Simulation of multiple review cycles and establishing quality control can boost the accuracy of AI models even further. Although our study aims at developing an annotation tool for diagnosing and classifying diabetic retinopathy fundus images but same concept can be used for fundus images of other ocular diseases as well as other streams of medical science such as radiology where image-based diagnostic applications are utilised.

Retrospective study on outcomes of terminal chop technique of nuclear fragmentation in phacoemulsification and combined surgery: An observational study

Purpose:

Terminal chop, a new technique of nuclear segmentation, has been recently described in literature. The technique is considered to be a safe and effective option.

Methods:

We did a retrospective study to evaluate the outcome of terminal chop. Patients operated for terminal chop (phacoemulsification with or without trabeculectomy) from October 2016 to July 2018 were included in the study.

Results:

In all, 933 patients were included in the study. The grade of nuclear sclerosis ranged from 2 to 5. All the patients had a significant visual improvement at 1 month postop. Around 3.4% had pseudoexfoliation and 8.89% of the eyes underwent combined surgery. Cumulative dissipated energy showed a proportionate relation with the grade of nucleus. Intraoperative complication rate was 0.08%. Postoperative complication rate was 3.50%.

Conclusion:

Terminal chop proved to be a safe, reproducible, and effective technique of nuclear segmentation in different grades of cataract.