An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images

Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F1 scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup

Genetic association study of selected candidate genes (ApoB, LPL, Leptin) and telomere length in obese and hypertensive individuals

<p>Abstract</p> <p>Background</p> <p>A genetic study was carried out among obese and hypertensive individuals from India to assess allelic association, if any, at three candidate loci: Apolipoprotein B (ApoB) minisatellite and two tetranucleotide repeat loci; LPL (Lipoprotein lipase) and Leptin. Attempt has also been made to find out whether telomere length attrition is associated with hypertension and obese individuals.</p> <p>Methods</p> <p>Venous blood samples were collected from 37 normal, 35 obese and 47 hypertensive individuals. Genomic DNA was extracted from peripheral blood mononuclear cells (PBMC) and PCR amplifications were achieved using locus specific primers. Genotyping of ApoB minisatellite was performed using 4% polyacrylamide gel electrophoresis (PAGE) followed by silver staining, whereas LPL and Leptin loci were genotyped using ALF Express™ DNA sequencer. Telomere length was determined using a recently developed real time based quantitative PCR, where the relative telomere length was determined by calculating the relative ratio of telomere (T) and single copy gene (S) PCR products which is expressed as T/S ratio.</p> <p>Results</p> <p>All the three loci are highly polymorphic, display high heterozygosity and conform to Hardy-Weinberg's equilibrium expectations. ApoB minisatellite displayed 14 alleles, whereas LPL and Leptin tetranucleotide loci were having 9 and 17 alleles, respectively. Interestingly two new alleles (9 and 11 repeats) were detected at ApoB locus for the first time. The alleles at Leptin locus were classified as Class I (lower alleles: 149-200 bp) and Class II alleles (higher alleles: >217 bp). Higher alleles at ApoB (>39 repeats), predominant allele 9 at LPL and alleles 164 bp and 224 bp at Leptin loci have shown allelic association with hypertensive individuals. After adjusting the influence of age and gender, the analysis of co-variance (ANCOVA) revealed the relative telomere length (T/S ratio) in hypertensive individuals to be (1.01 ± 0.021), which was significantly different (P < 0.001) from obese (1.20 ± 0.023) and normal (1.22 ± 0.014) individuals. However, no significant difference in the relative telomere length was observed among male and female individuals, although age related decrease in telomere length was observed in these limited sample size.</p> <p>Conclusion</p> <p>The present study revealed that allelic association at ApoB, LPL, Leptin loci and loss of telomere length may have strong genetic association with hypertensive individuals. However, further study on larger sample size is needed to draw firm conclusions.</p

Role of sequential chemical peel in cutaneous amyloidosis: a pilot case series

Summary Cutaneous amyloidosis can be a part of a systemic disease or can result from a localized process limited to the skin. It usually presents as pruritic hyperpigmented macules, papules or nodules, which are notoriously resistant to treatment. We performed a hospital-based pilot case series to assess the efficacy and safety of sequential salicylic acid (SA) and glycolic acid (GA) chemical peels. Patients underwent sequential chemical peel therapy with SA 20% and GA 35% used alternately each week for a total of 6 weeks. At subsequent follow-up visits, good clinical outcome and long-term maintenance was observed. This study highlights the promising role of chemical peels in this difficult and distressing disorder.</jats:p

OCULAR MANIFESTATIONS OF APLASTIC ANEMIA FOLLOWING PLATELET TRASFUSION: A CASE REPORT

AbstractAplastic anaemia is a rare haemopoietic stem-cell disorder that results in pancytopenia and hypocellular bone marrow. Ocular findings are manifestations of preexisting anemia. Here we are reporting a case of aplastic anemia which presented with the ocular findings following platelet transfusion which has not been reported in literature to the best of our knowledge.</jats:p

PHASE BASED ANCIENT DOCUMENT BINARIZATION OF CONTENT RETRIEVAL APPROACH

ABSTRACT We propose a model for image recovery from ancient documents. To achieve this, we combine three standard steps. They are, preprocessing, mai

Evaluation of Ocular Perfusion Pressure in Migraine Patients: A Case-control Study

Introduction: Vascular dysregulation is a common underlying factor between migraine and glaucoma. Fluctuations in Ocular Perfusion Pressure (OPP) can lead to ischaemia and damage to the optic nerve head. It is unclear if the risk of glaucoma in migraine patients is due to low OPP. Aim: To compare the OPP of migraine patients with healthy controls and assess the role of OPP as a risk factor for glaucoma in migraine patients. Materials and Methods: This case-control study was conducted from June 2019 to May 2020 at Yenepoya Medical College Hospital, Mangaluru, Karnataka, India. Adult patients of either gender, aged between 18 and 40 years, diagnosed with migraine, were included in the study. Intraocular Pressure (IOP), blood pressure, and OPP were measured in 94 migraine patients and compared with 94 age-matched controls. Data were analysed using Statistical Package for the Social Sciences (SPSS) version 23.0. Independent t-tests were used for comparison. Adjusted odds ratios were calculated using binary logistic regression, and a p-value <0.05 was considered statistically significant. Results: The mean IOP was 13.27±2.02 mmHg among cases and 13.38±1.87 mmHg among controls. The mean Systolic Blood Pressure (SBP) was 118.91±4.67 mmHg among cases and 116.09±5.18 mmHg among controls. The mean Diastolic Blood Pressure (DBP) was 77.72±4.48 mmHg among cases and 75.77±4.89 mmHg among controls. The mean OPP was 47.69±2.43 mmHg among cases and 46.08±2.46 mmHg among controls. The t-test values showed a statistically significant difference in SBP, DBP, and OPP between the two groups (p-value<0.05). Conclusion: The OPP in migraine patients differed significantly from that of the controls. However, further studies are needed to evaluate the relationship between perfusion pressure abnormalities and the risk of glaucoma in migraine patients

Artificial Neural Network in Prognosticating Human Personality from Social Networks

The analysis of text in the form of tweets, chat or posts can be an interesting as well as challenging area of research. In this paper, such an analysis provides information about the human behavior as positive, negative or neutral. For simplicity, tweets from social networking site, Twitter, are extracted for analyzing human personality. Various concepts from natural language processing, text mining and neural networks are used to establish the final outcome of the application. For analyzing text, Neural Networks are implemented which are so modeled that they predict the Human behavior as positive, negative or neutral based on extracted and preprocessed data. Using Neural Networks, the particular pattern is identified and weights are provided to words based on the extracted pattern.Neural networks have an added advantage of adaptive learning. This application can be immensely useful for politics, medical science, sports, matrimonial purposes etc.The results so obtained are quite promising