Secondary Childhood glaucoma – a rare association in Seckel syndrome

2021 ◽  
pp. 112067212110609
Author(s):  
Manju R Pillai ◽  
Srilekha Pallamparthy ◽  
Subathra Gnanavelu
Keyword(s):  
Vision Loss ◽  
Early Stage ◽  
Growth Delay ◽  
Facial Features ◽  
Seckel Syndrome ◽  
Visual Deterioration ◽  
Rare Association

A case of 12-year-old male with Seckel syndrome, presented with unilateral glaucoma leading to advanced disc damage hence, visual deterioration. Seckel syndrome being a rare inherited disorder characterized by growth delay and unique facial features, had been infrequently reported for ophthalmic anifestation especially glaucoma. Though glaucoma is a rare association in Seckel syndrome, screening at an early stage could help in preventing vision loss.

scholarly journals Classification among Microaneurysms, Exudates, and Lesion free Retinal Regions in the Eye Images using Transfer Learned CNNs

2019 ◽  
Vol 9 (2) ◽  
pp. 5508-5512
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Glucose Level ◽  
Vision Loss ◽  
Early Stage ◽  
Training Data ◽  
Fundus Images ◽  
Diabetic Mellitus ◽  
Start Process

When pancreas fails to secrete sufficient insulin in the human body, the glucose level in blood either becomes too high or too low. This fluctuation in glucose level affects different body organs such as kidney, brain, and eye. When the complications start appearing in the eyes due to Diabetic Mellitus (DM), it is called Diabetic Retinopathy (DR). DR can be categorized in several classes based on the severity, it can be Microaneurysms (ME), Haemorrhages (HE), Hard and Soft Exudates (EX and SE). DR is a slow start process that starts with very mild symptoms, becomes moderate with the time and results in complete vision loss, if not detected on time. Early-stage detection may greatly bolster in vision loss. However, it is impassable to detect the symptoms of DR with naked eyes. Ophthalmologist harbor to the several approaches and algorithm which makes use of different Machine Learning (ML) methods and classifiers to overcome this disease. The burgeoning insistence of Convolutional Neural Network (CNN) and their advancement in extracting features from different fundus images captivate several researchers to strive on it. Transfer Learning (TL) techniques help to use pre-trained CNN on a dataset that has finite training data, especially that in under developing countries. In this work, we propose several CNN architecture along with distinct classifiers which segregate the different lesions (ME and EX) in DR images with very eye-catching accuracies.

scholarly journals Diabetic retinopathy: treatment and prevention

2007 ◽  
Vol 4 (3_suppl) ◽  
pp. S9-S11 ◽  
Author(s):  
Paul M Dodson
Keyword(s):  
Diabetic Retinopathy ◽  
Standard Therapy ◽  
Vision Loss ◽  
Early Stage ◽  
Developed Countries ◽  
Relative Reduction ◽  
Treatment And Prevention ◽  
Working Age ◽  
Capillary Occlusion

Diabetic eye disease is the major cause of blindness and vision loss among working-age people in developed countries. Microangiopathy and capillary occlusion underlie the pathogenesis of disease. While laser treatment is regarded as the standard therapy, intensive medical management of glycaemia and hypertension is also a priority in order to reduce the risk of diabetic retinopathy. Recent data have prompted a re-evaluation of the role of lipid-modifying therapy in reducing diabetic retinopathy. The Fenofibrate Intervention for Event Lowering in Diabetes (FIELD) study demonstrated a significant 30% relative reduction in the need for first retinal laser therapy in patients with (predominantly early-stage) type 2 diabetes treated with fenofibrate 200 mg daily, from 5.2% with placebo to 3.6% with fenofibrate, p=0.0003. The benefit of fenofibrate was evident within the first year of treatment. These promising data justify further evaluation of the mechanism and role of fenofibrate, in addition to standard therapy, in the management of diabetic retinopathy.

scholarly journals Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Silky Goel ◽  
Siddharth Gupta ◽  
Avnish Panwar ◽  
Sunil Kumar ◽  
Madhushi Verma ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Fundus Photography ◽  
Huge Amount ◽  
Early Screening ◽  
Image Metadata ◽  
Very High

Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy.

scholarly journals Automatic Diagnosis of Glaucoma using Image Processing Technique

2019 ◽  
Vol 9 (2) ◽  
pp. 3984-3987
Keyword(s):  
Optic Nerve ◽  
Optic Disc ◽  
Vision Loss ◽  
Early Stage ◽  
Threshold Value ◽  
Processing Technique ◽  
Least Square ◽  
Disc Height ◽  
Image Processing Technique ◽  
Human Eye

Glaucoma is a human eye condition which will affect the optic nerve present in the retina. This condition occurs due to the abnormal ocular pressure in human eye. If it is not diagnosed and treated well in advance, it may lead to blindness. This is the major problem of elderly people all over the world. The best way to avoid vision loss due to glaucoma is to detect the disease at the early stage and treat it as soon as possible. These are the keys to prevent blindness. As vision is an important organ in human body it is advisable to keep it healthy. The optic cup in the retina will be pulled in towards the optic nerve away from the optic disc. At one point, the cup will be detached from the retina, causing blindness. So if one can monitor by measuring the optic disc to cup ratio, the progression of glaucoma can be diagnosed earlier. The proposed method detects the optic disc and cup using thresholding method. Direct least square fitting algorithm is used here to fit the ellipse in order to calculate the cup height and disc height. Then the ratio is calculated. If the calculated ratio is above the threshold value, it is considered as glaucoma affected eye otherwise not. The CDR is calculated using the formula VDH/VCH (Vertical Disc Height to the Vertical Cup Height). Thus, the proposed method helps to automatically detect the glaucoma disease with better sensitivity and specificity.

Visual deterioration after endonasal endoscopic skull base surgery: causes, treatments, and outcomes

2021 ◽  
pp. 1-11
Author(s):  
Joseph A. Carnevale ◽  
Christopher S. Babu ◽  
Jacob L. Goldberg ◽  
Reginald Fong ◽  
Theodore H. Schwartz
Keyword(s):  
Vision Loss ◽  
Rare Event ◽  
Visual Outcome ◽  
Supplemental Oxygen ◽  
Senior Author ◽  
Endoscopic Endonasal ◽  
Blood Pressure Elevation ◽  
Visual Deterioration ◽  
Associated Risk Factors ◽  
Early Reoperation

OBJECTIVE Visual deterioration after endoscopic endonasal transsphenoidal surgery (EETS) for sellar and parasellar masses is a rare but serious complication caused by either compressive or ischemic mechanisms. Timely diagnosis and intervention may restore vision if instituted appropriately. The associated risk factors and their relation to the success of intervention are not well understood. METHODS The authors examined a series of 1200 consecutive EETS cases performed by the senior author at Weill Cornell/NewYork-Presbyterian Hospital from 2010 to 2020. Cases with postoperative visual deterioration were identified. Pre- and postoperative clinical data, mechanism of visual decline, latency to intervention, and long-term visual outcome were retrospectively collected and analyzed with appropriate statistical methods. RESULTS Twenty-one patients (1.75%) complained of early postoperative visual deterioration. The most common pathology associated with postoperative visual loss was craniopharyngioma (7.69%), followed by meningioma (5.43%) and then pituitary adenoma (1.94%). Timely intervention restored vision in 81% of patients for a 0.33% rate of permanent visual deterioration. Average time to visual deterioration was 28.8 hours, and over 70% of patients experienced vision loss within the first 13 hours. Compressive etiology (n = 11), consisting of either hematoma (n = 8) or graft displacement (n = 3), occurred 7.3 hours and 70.3 hours after surgery, respectively, and was more common in adenomas. Acute postoperative visual deterioration was more common in firm closures (4.78%) compared with soft closures (1.03%; p = 0.0006). Ischemic etiology (n = 10) occurred 10.3 hours after surgery and was more common with craniopharyngiomas and meningiomas (p = 0.08). Sixteen patients (76.2%) underwent early reoperation to explore and decompress the optic apparatus. Vision was restored to baseline after reoperation in all 11 compressive cases, whereas 6/10 ischemic cases improved with supplemental oxygen and hypervolemic hypertensive therapy (p = 0.02). Fluid expansion from 8 to 16 hours (p = 0.034) and systolic blood pressure elevation from 32 to 48 hours (p = 0.05) after surgery were significantly higher in those ischemic patients who recovered some vision compared with those with persistent visual deficits. CONCLUSIONS Visual deterioration after EETS is a rare event but can be effectively treated if acted upon appropriately and in a timely fashion. Compressive etiology is reversible with early reoperation. Ischemic etiology can be successfully treated in roughly half of cases with supplemental oxygen and hypertensive hypervolemic therapy but may result in permanent visual deterioration if not instituted appropriately or if delayed with unnecessary exploratory surgery.

An Effective Approach Based on Improved Convolutionary Neural Network Classifier for the Detection of Diabetic Retinopathy

2021 ◽  
Vol 11 (12) ◽  
pp. 3082-3089
Author(s):  
B. Sakthi Karthi Durai ◽  
J. Benadict Raja
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Early Stage ◽  
Principal Component ◽  
Gabor Filtering ◽  
Processing Scheme ◽  
Successful Approach ◽  
Image Pixels ◽  
Watershed Method ◽  
In The Beginning

In diabetic individuals, diabetic retinopathy (DR) causes blindness. Therefore, detecting diabetic retinopathy at an early stage decreases vision loss. An successful approach for diabetic retinopathy prediction is discussed in this article. In the beginning, the input pictures of human retinal fundus images are preprocessed using histogram equalisation followed by Gabor filtering to reduce noise for enhancement. Then, using the Watershed method, segmentation is performed, and the features are retrieved through feature extraction. The best optimum features are selected using PCA (principal component analysis) approach. The morphological based post processing scheme was employed to further enhance the quality of selected features. At last, the classification approach is carried with the utilization of Google NET CNN classifier to classify/predict the retinal image as normal, abnormal, and severe. Google NET CNN has been developed with limited preprocessing step to distinguish visual features directly from image pixels. The findings are then evaluated and the efficacy of the new method is contrasted with other current methods. The quantitative findings were evaluated for Accuracy, precision, reliability, positive predictive levels and false predictive levels in parameters and were seen to deliver better results than current techniques.

Early-stage testicular cancer: A rare association with dermatomyositis

2010 ◽  
Vol 51 (2) ◽  
pp. 139-141 ◽  
Author(s):  
Eugene Tan ◽  
David Young ◽  
Blair McLaren ◽  
Alan Wright
Keyword(s):  
Testicular Cancer ◽  
Early Stage ◽  
Rare Association

scholarly journals Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 670
Author(s):  
Niloy Sikder ◽  
Mehedi Masud ◽  
Anupam Kumar Bairagi ◽  
Abu Shamim Mohammad Arif ◽  
Abdullah-Al Nahid ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Ensemble Learning ◽  
Vision Loss ◽  
Learning Algorithm ◽  
Early Stage ◽  
Texture Features ◽  
Asia Pacific ◽  
Health Concern ◽  
Feature Selection Technique ◽  
Margin Of Error

Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

scholarly journals Pemafibrate Protects Against Retinal Dysfunction in a Murine Model of Diabetic Retinopathy

2020 ◽  
Vol 21 (17) ◽  
pp. 6243 ◽  
Author(s):  
Yohei Tomita ◽  
Deokho Lee ◽  
Yukihiro Miwa ◽  
Xiaoyan Jiang ◽  
Masayuki Ohta ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Glucose ◽  
Vision Loss ◽  
Early Stage ◽  
Neural Function ◽  
Peroxisome Proliferator ◽  
Diabetic Mice ◽  
Peroxisome Proliferator Activated Receptor ◽  
Glucose Levels ◽  
Adult Mice

Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Retinal neuronal abnormalities occur in the early stage in DR. Therefore, maintaining retinal neuronal activity in DR may prevent vision loss. Previously, pemafibrate, a novel selective peroxisome proliferator-activated receptor alpha modulator, was suggested as a promising drug in hypertriglyceridemia. However, the role of pemafibrate remains obscure in DR. Therefore, we aimed to unravel systemic and retinal changes by pemafibrate in diabetes. Adult mice were intraperitoneally injected with streptozotocin (STZ) to induce diabetes. After STZ injection, diet supplemented with pemafibrate was given to STZ-induced diabetic mice for 12 weeks. During the experiment period, body weight and blood glucose levels were examined. Electroretinography was performed to check the retinal neural function. After sacrifice, the retina, liver, and blood samples were subjected to molecular analyses. We found pemafibrate mildly improved blood glucose level as well as lipid metabolism, boosted liver function, increased serum fibroblast growth factor21 level, restored retinal functional deficits, and increased retinal synaptophysin protein expression in STZ-induced diabetic mice. Our present data suggest a promising pemafibrate therapy for the prevention of early DR by improving systemic metabolism and protecting retinal function.

scholarly journals Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage

2021 ◽  
Vol 11 (6) ◽  
pp. 734
Author(s):  
Tania Akter ◽  
Mohammad Hanif Ali ◽  
Md. Imran Khan ◽  
Md. Shahriare Satu ◽  
Md. Jamal Uddin ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Developmental Disorder ◽  
Early Stage ◽  
Image Data ◽  
Autism Spectrum ◽  
Data Repository ◽  
Facial Features ◽  
Autistic Children ◽  
Face Images ◽  
Human Faces

Autism spectrum disorder (ASD) is a complex neuro-developmental disorder that affects social skills, language, speech and communication. Early detection of ASD individuals, especially children, could help to devise and strategize right therapeutic plan at right time. Human faces encode important markers that can be used to identify ASD by analyzing facial features, eye contact, and so on. In this work, an improved transfer-learning-based autism face recognition framework is proposed to identify kids with ASD in the early stages more precisely. Therefore, we have collected face images of children with ASD from the Kaggle data repository, and various machine learning and deep learning classifiers and other transfer-learning-based pre-trained models were applied. We observed that our improved MobileNet-V1 model demonstrates the best accuracy of 90.67% and the lowest 9.33% value of both fall-out and miss rate compared to the other classifiers and pre-trained models. Furthermore, this classifier is used to identify different ASD groups investigating only autism image data using k-means clustering technique. Thus, the improved MobileNet-V1 model showed the highest accuracy (92.10%) for k = 2 autism sub-types. We hope this model will be useful for physicians to detect autistic children more explicitly at the early stage.

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