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.

scholarly journals Artificial intelligence in dry eye disease

2021 ◽  
Author(s):  
Andrea M. Storås ◽  
Inga Strümke ◽  
Michael A. Riegler ◽  
Jakob Grauslund ◽  
Hugo L. Hammer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Dry Eye ◽  
Dry Eye Disease ◽  
Eye Disease

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.

scholarly journals Inhibition of Aberrant α(1,2)-Fucosylation at Ocular Surface Ameliorates Dry Eye Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7863
Author(s):  
Chang Ho Yoon ◽  
Jin Suk Ryu ◽  
Jung Hwa Ko ◽  
Joo Youn Oh
Keyword(s):  
Dry Eye ◽  
Ocular Surface ◽  
Meibomian Gland ◽  
Cellular Adhesion ◽  
Dry Eye Disease ◽  
Eye Disease ◽  
Corneal Epithelial ◽  
Wide Range ◽  
Novel Target ◽  
Desiccating Stress

Fucosylation is involved in a wide range of biological processes from cellular adhesion to immune regulation. Although the upregulation of fucosylated glycans was reported in diseased corneas, its implication in ocular surface disorders remains largely unknown. In this study, we analyzed the expression of a fucosylated glycan on the ocular surface in two mouse models of dry eye disease (DED), the NOD.B10.H2b mouse model and the environmental desiccating stress model. We furthermore investigated the effects of aberrant fucosylation inhibition on the ocular surface and DED. Results demonstrated that the level of type 2 H antigen, an α(1,2)-fucosylated glycan, was highly increased in the cornea and conjunctiva both in NOD.B10.H2b mice and in BALB/c mice subjected to desiccating stress. Inhibition of α(1,2)-fucosylation by 2-deoxy-D-galactose (2-D-gal) reduced corneal epithelial defects and increased tear production in both DED models. Moreover, 2-D-gal treatment suppressed the levels of inflammatory cytokines in the ocular surface and the percentages of IFN-γ+CD4+ cells in draining lymph nodes, whereas it did not affect the number of conjunctival goblet cells, the MUC5AC level or the meibomian gland area. Together, the findings indicate that aberrant fucosylation underlies the pathogenesis of DED and may be a novel target for DED therapy.

scholarly journals An Evidence-Based Strategic Approach to Prevention and Treatment of Dry Eye Disease, a Modern Global Epidemic

Healthcare ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 89
Author(s):  
Pragnya R. Donthineni ◽  
Swapna S. Shanbhag ◽  
Sayan Basu
Keyword(s):  
Dry Eye ◽  
Dry Eye Disease ◽  
Health Concern ◽  
Eye Disease ◽  
Extensive Literature ◽  
Preventive Strategies ◽  
Visual Rehabilitation ◽  
Global Epidemic ◽  
Strategic Approach ◽  
Wide Range

Dry eye disease (DED) is an emerging health concern causing significant visual, psychological, social, and economic impact globally. In contrast to visual rehabilitation undertaken at late stages of DED, measures instituted to prevent its onset, establishment, or progression can alter its natural course and effectively bring down the associated morbidity. This review attempts to present the available literature on preventive strategies of DED at one place, including strategies for risk assessment and mitigation, targeting a wide range of population. A literature search was conducted using PubMed and an extensive literature review on preventive strategies for DED was compiled to put forth a holistic and strategic approach for preventing DED. This can be undertaken at various stages or severity of DED directed at different tiers of the health care system. Conclusion: This review intends to put emphasis on preventive strategies being adopted as an integral part of routine clinical practice by general ophthalmologists and specialists to tackle the burden of DED and improve the quality of the lives of the patients suffering from it.

scholarly journals Artificial intelligence and echocardiography

2018 ◽  
pp. R115-R125 ◽  
Author(s):  
M Alsharqi ◽  
W J Woodward ◽  
J A Mumith ◽  
D C Markham ◽  
R Upton ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Error ◽  
Image Interpretation ◽  
Future Directions ◽  
Wide Range ◽  
Automated Interpretation ◽  
Artery Disease ◽  
Consistent Assessment ◽  
Ischaemic Burden

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

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