scholarly journals Diabetic Vitrectomy

2020 ◽  
Author(s):  
Ogugua N. Okonkwo
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Visual Impairment ◽  
Surgical Techniques ◽  
Surgical Removal ◽  
Disease Monitoring ◽  
Advanced Stage ◽  
Improved Outcomes ◽  
Diabetic Vitrectomy ◽  
Proliferative Disease

Diabetic retinopathy (DR) in its advanced stage is a leading cause of blindness and visual impairment. Despite efforts at early detection of DR, disease monitoring, and medical therapy, significant proportions of people living with diabetes still progress to develop the advanced proliferative disease, which is characterized by neovascularization, actively proliferating fibrovascular membranes, and retinal traction. The surgical removal of this proliferating tissue and the treatment of the retinal ischemic drive can be very rewarding, providing significant stability of the retina and in several cases improved retinal anatomy and vision. Diabetic vitrectomy comprises a broad range of surgical techniques and maneuvers, which offer the surgeon and patient opportunity to reverse deranged vitreoretinal anatomy and improve or stabilizes vision. Advances in vitreoretinal technology have contributed greatly to more recent improved outcomes; it is expected that future advances will offer even more benefit.

scholarly journals Excision of Lower Lip Mucocele Using Injection of Hydrocolloid Dental Impression Material in a Pediatric Patient: A Case Report

2021 ◽  
Vol 11 (13) ◽  
pp. 5819
Author(s):  
Gianluca Botticelli ◽  
Marco Severino ◽  
Gianmaria Fabrizio Ferrazzano ◽  
Pedro Vittorini Velasquez ◽  
Carlo Franceschini ◽  
...  
Keyword(s):  
Minor Salivary Gland ◽  
Surgical Techniques ◽  
Surgical Excision ◽  
Complete Healing ◽  
Surgical Removal ◽  
Clinical Approach ◽  
Lower Lip ◽  
Impression Material ◽  
Dental Impression

Oral mucocele is a benign cystic exophytic lesion affecting the minor salivary gland and is especially present in pediatric patients (3% under 14 years). It is characterized by an extravasation or retention of fluid or mucus in the submucosal tissue of the minor salivary glands. Several surgical techniques have been proposed over the years, including the excision of the mucocele by using the injection of a hydrocolloid impression material in the light of the cyst to prevent the collapse of the cystic wall and solidify the lesion, resulting in a better cleavage plan. The combined clinical approach between the combination of Shira’s technique and the surgical excision of the cystic lesion results in a conservative surgical removal of the lesion. Here, we reported the removal of a labial mucocele in a 14-year-old male patient, using the injection of a hydrocolloid impression material. At a 12 months follow up, the patient showed complete healing of the surgical site, showing a pinkish lip lining mucosa without scarring or recurrence of the primary lesion. The combined therapeutic approach between Shira’s technique and surgical excision allows a safe and predictable excision of the labial mucocele, minimizing the risk of recurrence.

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

Early detection of diabetic retinopathy from big data in hadoop framework

Displays ◽  
2021 ◽  
Vol 70 ◽  
pp. 102061
Author(s):  
Amartya Hatua ◽  
Badri Narayan Subudhi ◽  
Veerakumar T. ◽  
Ashish Ghosh
Keyword(s):  
Big Data ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Hadoop Framework

scholarly journals UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

2017 ◽  
Vol 10 (4) ◽  
pp. 32
Author(s):  
Srinivasan A ◽  
Sudha S
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Diabetic Retinopathy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Computation Time ◽  
Automated System ◽  
Morphological Operations ◽  
Advanced Stage ◽  
Sensitivity Specificity

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic. 

scholarly journals Automated Early Detection of Diabetic Retinopathy

Ophthalmology ◽  
2010 ◽  
Vol 117 (6) ◽  
pp. 1147-1154 ◽  
Author(s):  
Michael D. Abràmoff ◽  
Joseph M. Reinhardt ◽  
Stephen R. Russell ◽  
James C. Folk ◽  
Vinit B. Mahajan ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection

Feature extraction from retinal fundus image for early detection of diabetic retinopathy

2013 ◽  
Author(s):  
Syna Sreng ◽  
Jun-Ichi Takada ◽  
Noppadol Maneerat ◽  
Don Isarakorn ◽  
Ruttikorn Varakulsiripunth ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus

Surgical Prevention in Ovarian Cancer

2021 ◽  
pp. 194-206
Author(s):  
Alexios Papanikolaou ◽  
Anastasios Liberis ◽  
Anastasia Vatopoulou
Keyword(s):  
Ovarian Cancer ◽  
Primary Prevention ◽  
Early Detection ◽  
Early Diagnosis ◽  
Genital Tract ◽  
Female Genital Tract ◽  
Current Evidence ◽  
Advanced Stage ◽  
Stage Disease

Ovarian cancer is the second most common malignant disease of the female genital tract, but the first in mortality because it is usually diagnosed at an advanced stage. Options for early detection, diagnosis, and treatment are limited. Prevention of ovarian cancer relates to primary prevention by avoiding factors that are epidemiologically associated with an increased incidence of ovarian cancer and the adoption of protective habits. These include interventions to exclude the fallopian tubes and ovaries. Secondary prevention is related to early diagnosis. The chapter aims to summarize current evidence on prevention of ovarian cancer as well as role of surgery to prevent advanced-stage disease.

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