Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice

Mursch‐Edlmayr, Anna Sophie; Ng, Wai Siene; Diniz-Filho, Alberto; Sousa, David Cordeiro; Arnold, Louis; Schlenker, Matthew B.; Dueñas-Ángeles, Karla; Keane, Pearse A.; Crowston, Jonathan G; Jayaram, Hari

doi:10.1167/tvst.9.2.55

Cited by 74 publications

(72 citation statements)

References 120 publications

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“…Static automated perimetry is an integral component in the management and monitoring of glaucoma, and numerous studies in the literature have examined various perimetry metrics in search of an optimal marker of diagnosis or progression ( 22 ). In recent years, machine learning and neural networks have also been used in perimetry research ( 9 ); these algorithms are heavily dependent on well-categorized, large volume datasets. Thus, developing new perimetry metrics is an important focus of research in glaucoma ( 23 ), and access to structured perimetry is critical in facilitating this research ( 23 ).…”

Section: Discussionmentioning

confidence: 99%

“…One challenge in managing the large volume of perimetry data is obtaining accurate and detailed data points from each test ( 8 ). Therefore, most recent studies rely on small and single institution datasets containing hundreds of eyes ( 9 ). Few studies examining automated perimetry have datasets up to 2,000-3,000 eyes or more, with one study requiring the development of an in-house data extraction software system ( 10 , 11 ).…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools

Saifee

Liu

et al. 2021

Front. Med.

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Purpose: To introduce and validate hvf_extraction_script, an open-source software script for the automated extraction and structuring of metadata, value plot data, and percentile plot data from Humphrey visual field (HVF) report images.Methods: Validation was performed on 90 HVF reports over three different report layouts, including a total of 1,530 metadata fields, 15,536 value plot data points, and 10,210 percentile data points, between the computer script and four human extractors, compared against DICOM reference data. Computer extraction and human extraction were compared on extraction time as well as accuracy of extraction for metadata, value plot data, and percentile plot data.Results: Computer extraction required 4.9-8.9 s per report, compared to the 6.5-19 min required by human extractors, representing a more than 40-fold difference in extraction speed. Computer metadata extraction error rate varied from an aggregate 1.2-3.5%, compared to 0.2-9.2% for human metadata extraction across all layouts. Computer value data point extraction had an aggregate error rate of 0.9% for version 1, <0.01% in version 2, and 0.15% in version 3, compared to 0.8-9.2% aggregate error rate for human extraction. Computer percentile data point extraction similarly had very low error rates, with no errors occurring in version 1 and 2, and 0.06% error rate in version 3, compared to 0.06-12.2% error rate for human extraction.Conclusions: This study introduces and validates hvf_extraction_script, an open-source tool for fast, accurate, automated data extraction of HVF reports to facilitate analysis of large-volume HVF datasets, and demonstrates the value of image processing tools in facilitating faster and cheaper large-volume data extraction in research settings.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools

Saifee

Liu

et al. 2021

Front. Med.

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“…For glaucoma, AI can predict progression earlier than conventional methods, but incorporation into the health system is needed so that it makes the most out of the benefits of AI [74]. AI can act as a form of teleophthalmological care for glaucoma, collecting data from devices that the patients use at home such as self-monitoring of intraocular pressure tonometry [75], smartphone-based head-mounted perimeter for detection of VF defects [76], ophthalmoscopic applications for smartphones and processing this data to result in a diagnosis or progression risk percentage [77].…”

Section: Glaucomamentioning

confidence: 99%

“…Diagnostic accuracy, screening effectiveness and evaluation of progression risk can be facilitated by AI technologies using deep learning algorithms as presented by Mursch-Edlmayr et al Specificity and sensitivity for OCT imaging, fundus photography and visual field (VF) testing are increased when using AI. For glaucoma, AI can predict progression earlier than conventional methods, but incorporation into the health system is needed so that it makes the most out of the benefits of AI [ 74 ]. AI can act as a form of teleophthalmological care for glaucoma, collecting data from devices that the patients use at home such as self-monitoring of intraocular pressure tonometry [ 75 ], smartphone-based head-mounted perimeter for detection of VF defects [ 76 ], ophthalmoscopic applications for smartphones and processing this data to result in a diagnosis or progression risk percentage [ 77 ].…”

Section: Reviewmentioning

confidence: 99%

Teleophthalmology and Artificial Intelligence As Game Changers in Ophthalmic Care After the COVID-19 Pandemic

Nikolaidou¹,

Tsaousis

2021

Cureus

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The current COVID-19 pandemic has boosted a sudden demand for telemedicine due to quarantine and travel restrictions. The exponential increase in the use of telemedicine is expected to affect ophthalmology drastically. The aim of this review is to discuss the utility, effectiveness and challenges of teleophthalmological new tools for eyecare delivery as well as its implementation and possible facilitation with artificial intelligence. We used the terms: "teleophthalmology," "telemedicine and COVID-19," "retinal diseases and telemedicine," "virtual ophthalmology," "cost effectiveness of teleophthalmology," "pediatric teleophthalmology," "Artificial intelligence and ophthalmology," "Glaucoma and teleophthalmology" and "teleophthalmology limitations" in the database of PubMed and selected the articles being published in the course of 2015-2020. After the initial search, 321 articles returned as relevant. A meticulous screening followed and eventually 103 published manuscripts were included and used as our references. Emerging in the market, teleophthalmology is showing great potential for the future of ophthalmological care, benefiting both patients and ophthalmologists in times of pandemics. The spectrum of eye diseases that could benefit from teleophthalmology is wide, including mostly retinal diseases such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration but also glaucoma and anterior segment conditions. Simultaneously, artificial intelligence provides ways of implementing teleophthalmology easier and with better outcomes, contributing as significant changing factors for ophthalmology practice after the COVID-19 pandemic.

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“…Also the traditional machine learning models areused for glaucoma prediction [7][8][9].Researchers comprehensively reviewed in their different articles about glaucoma, its types, cause, effect, and possible treatments. They used clinically as well as image processing, machine learning, and deep learning techniques to detect this disease effectively [10][11]…”

Section: Related Workmentioning

confidence: 99%

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

Bandyopadhyay¹,

Bose²,

Dutta³

et al. 2021

Preprint

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In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

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Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice

Cited by 74 publications

References 120 publications

Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools

Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools

Teleophthalmology and Artificial Intelligence As Game Changers in Ophthalmic Care After the COVID-19 Pandemic

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

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