Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss

Zhalechian, Mohammad; Oyen, Mark P. Van; Lavieri, Mariel S.; Moraes, Carlos Gustavo De; Girkin, Christopher A.; Fazio, Massimo A.; Weinreb, Robert N.; Bowd, Christopher; Liebmann, Jeffrey M.; Zangwill, Linda M.; Andrews, Christopher A.; Stein, Joshua D.

doi:10.1016/j.xops.2021.100097

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Two Kalman filters (KF) were constructed Mohammed et al [136] to forecast mean deviation (MD) and pattern standard deviation values 36 months in the future for patients with OAG and glaucoma suspects: Both a KF with tonometry and perimetry data (KF-TP) and a KF with tonometry, perimetry, and global RNFL data (KF-TPO) were used. Prediction accuracy (proportion of MD values predicted within the 95% repeatability interval) across the models, differences.…”

Section: B) Glaucoma Prediction Using Machine Learning Techniquementioning

confidence: 99%

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Mathew¹,

Ilango

Asha³

2023

IJRITCC

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Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment.

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Section: B) Glaucoma Prediction Using Machine Learning Techniquementioning

confidence: 99%

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Mathew¹,

Ilango

Asha³

2023

IJRITCC

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Convolutional Neural Network Analysis of Fundus for Glaucoma Diagnosis

Daineko

Zarina

2022

2022 International Conference on Smart Information Systems and Technologies (SIST)

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Application of artificial intelligence in glaucoma care: An updated review

Wu,

Lin,

Moghimi

2024

Taiwan Journal of Ophthalmology

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The application of artificial intelligence (AI) in ophthalmology has been increasingly explored in the past decade. Numerous studies have shown promising results supporting the utility of AI to improve the management of ophthalmic diseases, and glaucoma is of no exception. Glaucoma is an irreversible vision condition with insidious onset, complex pathophysiology, and chronic treatment. Since there remain various challenges in the clinical management of glaucoma, the potential role of AI in facilitating glaucoma care has garnered significant attention. In this study, we reviewed the relevant literature published in recent years that investigated the application of AI in glaucoma management. The main aspects of AI applications that will be discussed include glaucoma risk prediction, glaucoma detection and diagnosis, visual field estimation and pattern analysis, glaucoma progression detection, and other applications.

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Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss

Cited by 3 publications

References 31 publications

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Convolutional Neural Network Analysis of Fundus for Glaucoma Diagnosis

Application of artificial intelligence in glaucoma care: An updated review

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