scholarly journals Explanatory Model of Dry Eye Disease Using Health and Nutrition Examinations: Machine Learning and Network-Based Factor Analysis From a National Survey

10.2196/16153 ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. e16153
Author(s):  
Sang Min Nam ◽  
Thomas A Peterson ◽  
Atul J Butte ◽  
Kyoung Yul Seo ◽  
Hyun Wook Han
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Dry Eye ◽  
Associated Factors ◽  
Dry Eye Disease ◽  
Explanatory Model ◽  
Patient Specific ◽  
Eye Disease ◽  
Omega 3 ◽  
Health And Nutrition

Background Dry eye disease (DED) is a complex disease of the ocular surface, and its associated factors are important for understanding and effectively treating DED. Objective This study aimed to provide an integrative and personalized model of DED by making an explanatory model of DED using as many factors as possible from the Korea National Health and Nutrition Examination Survey (KNHANES) data. Methods Using KNHANES data for 2012 (4391 sample cases), a point-based scoring system was created for ranking factors associated with DED and assessing patient-specific DED risk. First, decision trees and lasso were used to classify continuous factors and to select important factors, respectively. Next, a survey-weighted multiple logistic regression was trained using these factors, and points were assigned using the regression coefficients. Finally, network graphs of partial correlations between factors were utilized to study the interrelatedness of DED-associated factors. Results The point-based model achieved an area under the curve of 0.70 (95% CI 0.61-0.78), and 13 of 78 factors considered were chosen. Important factors included sex (+9 points for women), corneal refractive surgery (+9 points), current depression (+7 points), cataract surgery (+7 points), stress (+6 points), age (54-66 years; +4 points), rhinitis (+4 points), lipid-lowering medication (+4 points), and intake of omega-3 (0.43%-0.65% kcal/day; −4 points). Among these, the age group 54 to 66 years had high centrality in the network, whereas omega-3 had low centrality. Conclusions Integrative understanding of DED was possible using the machine learning–based model and network-based factor analysis. This method for finding important risk factors and identifying patient-specific risk could be applied to other multifactorial diseases.

Effect of a Novel Omega-3 and Omega-6 Fatty Acid Supplement on Dry Eye Disease

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Alison Ng ◽  
Jill Woods ◽  
Theresa Jahn ◽  
Lyndon W. Jones ◽  
Jenna Sullivan Ritter
Keyword(s):  
Fatty Acid ◽  
Dry Eye ◽  
Dry Eye Disease ◽  
Eye Disease ◽  
Omega 3 ◽  
Acid Supplement ◽  
Omega 6 ◽  
Fatty Acid Supplement

scholarly journals Dry eye, its clinical subtypes and associated factors in healthy pregnancy: A cross-sectional study

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258233
Author(s):  
Kofi Asiedu ◽  
Samuel Kyei ◽  
Madison Adanusa ◽  
Richard Kobina Dadzie Ephraim ◽  
Stephen Animful ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Pregnant Women ◽  
Dry Eye ◽  
Associated Factors ◽  
Cross Sectional Study ◽  
Dry Eye Disease ◽  
Eye Disease ◽  
Sectional Study ◽  
Cross Sectional ◽  
Evaporative Dry Eye

The study determined the frequency of dry eye, its clinical subtypes and risk factors among pregnant women. This study was a hospital-based cross-sectional study of pregnant women visiting the antenatal clinic of the University of Cape Coast hospital. Clinical dry eye tests were performed along with the administration of a symptom questionnaire. Frequencies, chi-square analysis and logistic regression analyses were conducted to determine the frequency of dry eye disease, its clinical subtypes and associated factors. The prevalence of dry eye disease among the cohort of pregnant women was 82/201 (40.8% 95% confidence interval 34.3%-47.3%). Among the 82 pregnant women with dry eye disease, the frequencies of the clinical subtypes of dry eye were: evaporative dry eye [15/82(18.3%; 95% CI, 12.2%–25.2%)], aqueous deficient dry eye [10/82(12.2.%; 95% CI, 7.3%–18.3)], mixed dry eye [6/82(7.3%; 95% CI, 3.7%–11.0%)], and unclassified dry eye [51/82(62.2%; 95% CI, 52.4%–72.0%)]. Binary logistic regression analysis showed that the following factors were not significantly associated with dry eye: age, BMI, lipid profile, prolactin level, testosterone level, ocular protection index and blink rate. Only gestational age was significantly associated with dry eye disease in pregnancy. In conclusion, the current study showed that dry eye disease occurs frequently in pregnant women ranging from the first to the third trimester and it is associated with increasing gestational age. The evaporative dry eye was more common compared to the aqueous deficient dry eye, but most dry eye could not be classified.

scholarly journals Omega-3 supplementation is neuroprotective to corneal nerves in dry eye disease: a pilot study

2017 ◽  
Vol 37 (4) ◽  
pp. 473-481 ◽  
Author(s):  
Holly R. Chinnery ◽  
Cecilia Naranjo Golborne ◽  
Laura E. Downie
Keyword(s):  
Pilot Study ◽  
Dry Eye ◽  
Dry Eye Disease ◽  
Eye Disease ◽  
Omega 3 ◽  
Corneal Nerves

scholarly journals Artificial Intelligence in Dry Eye Disease

2021 ◽  
Author(s):  
Andrea Marheim Storaas ◽  
Inga Strumke ◽  
Michael Riegler ◽  
Jakob Grauslund ◽  
Hugo Hammer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Dry Eye ◽  
Model Development ◽  
Image Interpretation ◽  
Dry Eye Disease ◽  
Machine Learning Techniques ◽  
Eye Disease ◽  
Recent Success ◽  
Wide Range

Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term `AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.

Omega-3 fatty acid supplements do not improve symptoms of dry eye disease

2018 ◽  
Vol 56 (12) ◽  
pp. 144-144 ◽  
Keyword(s):  
Fatty Acid ◽  
Dry Eye ◽  
Research Group ◽  
Dry Eye Disease ◽  
Eye Disease ◽  
Omega 3 ◽  
Fatty Acid Supplementation ◽  
Acid Supplementation ◽  
Omega 3 Fatty Acid ◽  
Study Research

Review of: The Dry Eye Assessment and Management Study Research Group. N-3 fatty acid supplementation for the treatment of dry eye disease. NEJM 2018;378:1681–90.

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