scholarly journals Screening for Diabetic Retinopathy Using a Portable, Noncontact, Nonmydriatic Handheld Retinal Camera

2016 ◽  
Vol 11 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Wenlan Zhang ◽  
Peter Nicholas ◽  
Stefanie Gail Schuman ◽  
Michael John Allingham ◽  
Ambar Faridi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Prospective Study ◽  
Low Vision ◽  
High Sensitivity ◽  
Retinal Images ◽  
Training Experience ◽  
Clinical Exam ◽  
Eye Exams ◽  
A Prospective Study ◽  
Different Levels

Background: Diabetic retinopathy (DR) is a leading cause of low vision and blindness. We evaluated the feasibility of using a handheld, noncontact digital retinal camera, Pictor, to obtain retinal images in dilated and undilated eyes for DR screening. We also evaluated the accuracy of ophthalmologists with different levels of training/experience in grading these images to identify eyes with vision-threatening DR. Methods: A prospective study of diabetic adults scheduled to have dilated eye exams at Duke Eye Center from January to May 2014 was conducted. An imager acquired retinal images pre- and postdilation with Pictor and selected 1 pre- and 1 postdilation image per eye. Five masked ophthalmologists graded images for gradability (based on image focus and centration) and the presence of no, mild, moderate, or severe nonproliferative DR (NPDR) or proliferative DR (PDR). Referable disease was defined as moderate or severe NPDR or PDR on image grading. We evaluated feasibility based on the graders’ evaluation of image gradability. We evaluated accuracy of identifying vision-threatening disease (severe NPDR or PDR documented on dilated clinical examination) based on the graders’ sensitivity and specificity of grading referable disease. Results: Images were gradable in 86-94% of predilation and 94-97% of postdilation photos. Compared to the dilated clinical exam, overall sensitivity for identifying vision-threatening DR was 64-88% and specificity was 71-90%. Conclusions: Pictor can capture retinal images of sufficient quality to screen for DR with and without dilation. Single retinal images obtained using Pictor can identify eyes with vision-threatening DR with high sensitivity and acceptable specificity compared to clinical exam.

scholarly journals Anemia predicts poor outcomes of COVID-19 in hospitalized patients: a prospective study in Iran

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Masood Faghih Dinevari ◽  
Mohammad Hossein Somi ◽  
Elham Sadeghi Majd ◽  
Mahdieh Abbasalizad Farhangi ◽  
Zeinab Nikniaz
Keyword(s):  
Prospective Study ◽  
High Sensitivity ◽  
Hospitalized Patients ◽  
Icu Admission ◽  
Reactive Protein ◽  
Hs Crp ◽  
Prospective Association ◽  
Poor Outcomes ◽  
A Prospective Study ◽  
Ventilator Requirement

Abstract Background There are limited number of studies with controversial findings regarding the association between anemia at admission and coronavirus disease 2019 (COVID-19) outcomes. Therefore, in this research, we aimed to investigate the prospective association between anemia and COVID-19 outcomes in hospitalized patients in Iran. Methods In this prospective study, the data of 1274 consecutive patients hospitalized due to COVID-19 were statistically analyzed. All biomarkers, including hemoglobin and high-sensitivity C-reactive protein (hs-CRP) levels were measured using standard methods. Anemia was defined as a hemoglobin (Hb) concentration of less than 13 g/dL and 12 g/dL in males and females, respectively. Assessing the association between anemia and COVID-19 survival in hospitalized patients was our primary endpoint. Results The mean age of the participants was 64.43 ± 17.16 years, out of whom 615 (48.27%) were anemic subjects. Patients with anemia were significantly older (P = 0.02) and had a higher frequency of cardiovascular diseases, hypertension, kidney disease, diabetes, and cancer (P < 0.05). The frequency of death (anemic: 23.9% vs. nonanemic: 13.8%), ICU admission (anemic: 27.8% vs. nonanemic:14.71%), and ventilator requirement (anemic: 35.93% vs. nonanemic: 20.63%) were significantly higher in anemic patients than in nonanemic patients (P < 0.001). According to the results of regression analysis, after adjusting for significant covariate in the univariable model, anemia was independently associated with mortality (OR: 1.68, 95% CI: 1.10, 2.57, P = 0.01), ventilator requirement (OR: 1.74, 95% CI: 1.19, 2.54, P = 0.004), and the risk of ICU admission (OR: 2.06, 95% CI: 1.46, 2.90, P < 0.001). Conclusion The prevalence of anemia in hospitalized patients with COVID-19 was high and was associated with poor outcomes of COVID-19.

scholarly journals Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

2020 ◽  
Vol 8 (1) ◽  
pp. e000892 ◽  
Author(s):  
Bhavana Sosale ◽  
Sosale Ramachandra Aravind ◽  
Hemanth Murthy ◽  
Srikanth Narayana ◽  
Usha Sharma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Smart Phone ◽  
High Sensitivity ◽  
Ground Truth ◽  
Cross Sectional Study ◽  
Retinal Images ◽  
Cross Sectional ◽  
Smart Study

IntroductionThe aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.MethodsThis cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).ResultsAnalysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3% (95% CI 80.9% to 85.7%) and 95.5% (95% CI 94.1% to 96.8%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93% (95% CI 91.3% to 94.7%) and 92.5% (95% CI 90.8% to 94.2%).ConclusionThe Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.

scholarly journals Offering pregnant women different levels of genetic information from prenatal chromosome microarray: a prospective study

2018 ◽  
Vol 26 (4) ◽  
pp. 485-494 ◽  
Author(s):  
Jane L. Halliday ◽  
Cecile Muller ◽  
Taryn Charles ◽  
Fiona Norris ◽  
Joanne Kennedy ◽  
...  
Keyword(s):  
Prospective Study ◽  
Pregnant Women ◽  
Genetic Information ◽  
A Prospective Study ◽  
Different Levels ◽  
Chromosome Microarray

scholarly journals Role of procalcitonin and high-sensitivity C-reactive protein in sepsis: a prospective study

Critical Care ◽  
2011 ◽  
Vol 15 (S3) ◽  
Author(s):  
S Das ◽  
D Anand ◽  
S Ray ◽  
S Bhargava ◽  
A Manocha ◽  
...  
Keyword(s):  
Prospective Study ◽  
High Sensitivity ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
A Prospective Study

scholarly journals A Machine Learning Approach for the Diagnosis of Diabetes : A Review

2020 ◽  
pp. 509-516
Author(s):  
Pravin S. Rahate ◽  
Nikhat Raza
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Selection Process ◽  
High Sensitivity ◽  
Healthcare Services ◽  
Automated System ◽  
Retinal Images ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Patients With Diabetes

Diabetes mellitus (DM) is a chronic disease that affects 382 million patients’ worldwide (2013 data) and is predicted to increase to as many as 592 million adults by 2035. DM is one of the major causes of blindness in young adults around the world. The most serious ocular complication of DM is diabetic retinopathy (DR).Diabetic retinopathy is the most common microvascular complication in diabetes1, for the screening of which the retinal imaging is the most widely used method due to its high sensitivity in detecting retinopathy. Prompt diagnosis is important through efficient screening. The evaluation of the severity and degree of retinopathy associated with a person having diabetes is currently performed by medical experts based on the fundus or retinal images of the patient’s eyes As the number of patients with diabetes is rapidly increasing, the number of retinal images produced by the screening programmes will also increase, which in turn introduces a large labor-intensive burden on the medical experts as well as cost to the healthcare services. Manual grading of these images to determine the severity of DR is rather slow and resource demanding. This could be alleviated with an automated system either as support for medical experts’ work or as full diagnosis tool. This labor-intensive task could greatly benefit from automatic detection using machine learning technique. Early detection and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. The objective of this paper is to explore the work happening on the detection, progression and feature selection process for the prediction of DR and to establish the extent and depth of existing knowledge on RD prediction process.

Can High-Sensitivity C-Reactive Protein and Ferritin Predict Functional Outcome in Acute Ischemic Stroke? A Prospective Study

2013 ◽  
Vol 20 (6) ◽  
pp. 528-536 ◽  
Author(s):  
Aslihan Kusvuran Ozkan ◽  
Oya Umit Yemisci ◽  
Sacide Nur Saracgil Cosar ◽  
Pinar Oztop ◽  
Nur Turhan
Keyword(s):  
Ischemic Stroke ◽  
Prospective Study ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
High Sensitivity ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
A Prospective Study
Sign in / Sign up

Export Citation Format

Share Document