Comparison of the efficacy of lotrafilcon B and comfilcon A silicone hydrogel bandage contact lenses after transepithelial photorefractive keratectomy

Background: At completion of transepithelial photorefractive keratectomy (t-PRK) surgery, the eye is usually fitted with a bandage contact lens to reduce discomfort and promote epithelial healing. This study aimed to compare the outcomes of eyes fitted with lotrafilcon B versus comfilcon A, silicone hydrogel bandage contact lenses after t-PRK for the correction of low to moderate myopia, with or without astigmatism. Methods: In this comparative, prospective study, patients with myopia < -6 D with or without astigmatism (< 1.75 D), who underwent t-PRK between January and June 2018, were randomly allocated to the lotrafilcon B and comfilcon A groups. Preoperative characteristics, including age, sex, eye treated, uncorrected visual acuity (UCVA), best-corrected visual acuity, mesopic pupil size, central corneal thickness, and refractive error were recorded. Postoperatively, pain score, UCVA, and corneal epithelial defect size on days 1, 4, and 7 were compared between the two groups. Results: Twenty-nine eyes were included in each group. Demographic characteristics and preoperative measurements were similar between the two groups. UCVA was significantly improved on day 7 as compared to day 1 in the comfilcon A group (P = 0.03), but remained the same in the lotrafilcon B group (P = 0.70) as on day 1 postoperatively. There was no significant difference in UCVA between the two groups at any follow-up visits (all P > 0.05). The pain score on the first postoperative day was significantly higher in the lotrafilcon B-fitted eyes than in the comfilcon A group (P < 0.001), but was significantly reduced in both groups compared to day 1 (both P < 0.001). The epithelial defect in the comfilcon A group was significantly greater than in the lotrafilcon B group (P < 0.001) at day 1 postoperatively, with significant improvement in both groups (both P < 0.001). Conclusions: Healing responses were better with lotrafilcon B than with comfilcon A bandage contact lenses. The patients had a greater mean pain score with lotrafilcon B than with comfilcon A lenses on the first postoperative day, yet the final outcome was comparable between the two groups. We did not encounter any postoperative complications related to contact lens wear

A new RSA public key encryption scheme with chaotic maps

Public key cryptography has received great attention in the field of information exchange through insecure channels. In this paper, we combine the Dependent-RSA (DRSA) and chaotic maps (CM) to get a new secure cryptosystem, which depends on both integer factorization and chaotic maps discrete logarithm (CMDL). Using this new system, the scammer has to go through two levels of reverse engineering, concurrently, so as to perform the recovery of original text from the cipher-text has been received. Thus, this new system is supposed to be more sophisticated and more secure than other systems. We prove that our new cryptosystem does not increase the overhead in performing the encryption process or the decryption process considering that it requires minimum operations in both. We show that this new cryptosystem is more efficient in terms of performance compared with other encryption systems, which makes it more suitable for nodes with limited computational ability

Combining Artificial Intelligence and Image Processing for Diagnosing Diabetic Retinopathy in Retinal Fundus Images

Retinopathy is an eye disease caused by diabetes, and early detection and treatment can potentially reduce the risk of blindness in diabetic retinopathy sufferers. Using retinal Fundus images, diabetic retinopathy can be diagnosed, recognized, and treated. In the current state of the art, sensitivity and specificity are lacking. However, there are still a number of problems to be solved in state-of-the-art techniques like performance, accuracy, and being able to identify DR disease effectively with greater accuracy. In this paper, we have developed a new approach based on a combination of image processing and artificial intelligence that will meet the performance criteria for the detection of disease-causing diabetes retinopathy in Fundus images. Automatic detection of diabetic retinopathy has been proposed and has been carried out in several stages. The analysis was carried out in MATLAB using software-based simulation, and the results were then compared with those of expert ophthalmologists to verify their accuracy. Different types of diabetic retinopathy are represented in the experimental evaluation, including exudates, micro-aneurysms, and retinal hemorrhages. The detection accuracies shown by the experiments are greater than 98.80 percent

Swin Transformer-Based Segmentation and Multi-Scale Feature Pyramid Fusion Module for Alzheimer’s Disease with Machine Learning

Alzheimer Disease (AD) is the ordinary type of dementia which does not have any proper and efficient medication. Accurate classification and detection of AD helps to diagnose AD in an earlier stage, for that purpose machine learning and deep learning techniques are used in AD detection which observers both normal and abnormal brain and accurately detect AD in an early. For accurate detection of AD, we proposed a novel approach for detecting AD using MRI images. The proposed work includes three processes such as tri-level pre-processing, swin transfer based segmentation, and multi-scale feature pyramid fusion module-based AD detection.In pre-processing, noises are removed from the MRI images using Hybrid Kuan Filter and Improved Frost Filter (HKIF) algorithm, skull stripping is performed by Geodesic Active Contour (GAC) algorithm which removes the non-brain tissues that increases detection accuracy. Here, bias field correction is performed by Expectation-Maximization (EM) algorithm which removes the intensity non-uniformity. After completed pre-processing, we initiate segmentation process using Swin Transformer based Segmentation using Modified U-Net and Generative Adversarial Network (ST-MUNet) algorithm which segments the gray matter, white matter, and cerebrospinal fluid from the brain images by considering cortical thickness, color, texture, and boundary information which increases segmentation accuracy. After that, multi-scale feature extraction is performed by Multi-Scale Feature Pyramid Fusion Module using VGG16 (MSFP-VGG16) which extract the features in multi-scale which increases the detection and classification accuracy, based on the extracted features the brain image is classified into three classes such as Alzheimer Disease (AD), Mild Cognitive Impairment, and Normal. The simulation of this research is conducted by Matlab R2020a simulation tool, and the performance of this research is evaluated by ADNI dataset in terms of accuracy, specificity, sensitivity, confusion matrix, and positive predictive value.