The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma

Kapoor, Rahul; Whigham, Benjamin T.; Al-Aswad, Lama A.

doi:10.1007/s40135-019-00209-w

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…Color fundus photographs, and the vast quantities of clinical images acquired by OCT are ideal modalities for AI-DLS applications. Diagnostic accuracy of various AI-DLS in ophthalmic disease has been proven by a number of studies [72]. More importantly, recent studies have shown that AI-DLS is able to process large amounts of digital datasets beyond the limit of human capacity and can identify retinal features not seen by human eyes, analyze them and convert this data into useful clinical information.…”

Section: Current Challenges and Opportunitiesmentioning

confidence: 99%

Artificial intelligence in ophthalmology: an insight into neurodegenerative disease

Patil¹,

Biousse²,

Newman

2022

Current Opinion in Ophthalmology

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Purpose of reviewThe aging world population accounts for the increasing prevalence of neurodegenerative diseases such as Alzheimer's and Parkinson's which carry a significant health and economic burden. There is therefore a need for sensitive and specific noninvasive biomarkers for early diagnosis and monitoring. Advances in retinal and optic nerve multimodal imaging as well as the development of artificial intelligence deep learning systems (AI-DLS) have heralded a number of promising advances of which ophthalmologists are at the forefront.Recent findingsThe association among retinal vascular, nerve fiber layer, and macular findings in neurodegenerative disease is well established. In order to optimize the use of these ophthalmic parameters as biomarkers, validated AI-DLS are required to ensure clinical efficacy and reliability. Varied image acquisition methods and protocols as well as variability in neurogenerative disease diagnosis compromise the robustness of ground truths that are paramount to developing high-quality training datasets.SummaryIn order to produce effective AI-DLS for the diagnosis and monitoring of neurodegenerative disease, multicenter international collaboration is required to prospectively produce large inclusive datasets, acquired through standardized methods and protocols. With a uniform approach, the efficacy of resultant clinical applications will be maximized.

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Section: Current Challenges and Opportunitiesmentioning

confidence: 99%

Artificial intelligence in ophthalmology: an insight into neurodegenerative disease

Patil¹,

Biousse²,

Newman

2022

Current Opinion in Ophthalmology

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“…To date, AI use in the context of AMD has been primarily for review of fundus photography, optical coherence tomography, and other ocular imaging modalities. 3,5,[18][19][20][21] However, AI application is expanding to biofluid biomarker analysis, enabling improved exploration of molecular AMD etiology, which could support an array of AMD clinical tools and spur therapeutic advances. [22][23][24] Clinical tools built with AI could allow for mass screening of AMD, earlier intervention and monitoring, and subsequently, improved personalized treatments for better patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

Pucchio¹,

Krance²,

Pur³

et al. 2022

OPTH

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This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case–control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.

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“…All of these techniques have been implicated in diagnosis, monitoring glaucomatous progression, treatment selection, and differentiation between glaucoma and other ophthalmic conditions. [17][18][19][20][21][22][23][24] Traditional imaging focused AI applications have rivaled the diagnostic ability of trained ophthalmologists in glaucoma diagnosis using optical coherence tomography (OCT) or fundoscopy. [17][18][19][20][21][22][23][24] In more recent research efforts, biofluid marker analysis using AI is being investigated to develop more complex and complete clinical tools that may serve as point of care diagnostic tools, determination of underlying glaucoma etiology, and prediction of glaucomatous progression.…”

Section: Introductionmentioning

confidence: 99%

“…[17][18][19][20][21][22][23][24] Traditional imaging focused AI applications have rivaled the diagnostic ability of trained ophthalmologists in glaucoma diagnosis using optical coherence tomography (OCT) or fundoscopy. [17][18][19][20][21][22][23][24] In more recent research efforts, biofluid marker analysis using AI is being investigated to develop more complex and complete clinical tools that may serve as point of care diagnostic tools, determination of underlying glaucoma etiology, and prediction of glaucomatous progression. [25][26][27] Clinical tools using AI could allow for automated glaucoma screening at primary care facilities or allied eye care providers, leading to improved patient outcomes and efficient use of specialist time and resources.…”

Section: Introductionmentioning

confidence: 99%

The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review

Pucchio

Krance

Pur

et al. 2022

European Journal of Ophthalmology

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Purpose This review focuses on utility of artificial intelligence (AI) in analysis of biofluid markers in glaucoma. We detail the accuracy and validity of AI in the exploration of biomarkers to provide insight into glaucoma pathogenesis. Methods A comprehensive search was conducted across five electronic databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science. Studies pertaining to biofluid marker analysis using AI or bioinformatics in glaucoma were included. Identified studies were critically appraised and assessed for risk of bias using the Joanna Briggs Institute Critical Appraisal tools. Results A total of 10,258 studies were screened and 39 studies met the inclusion criteria, including 23 cross-sectional studies (59%), nine prospective cohort studies (23%), six retrospective cohort studies (15%), and one case-control study (3%). Primary open angle glaucoma (POAG) was the most commonly studied subtype (55% of included studies). Twenty-four studies examined disease characteristics, 10 explored treatment decisions, and 5 provided diagnostic clarification. While studies examined at entire metabolomic or proteomic profiles to determine changes in POAG, there was heterogeneity in the data with over 175 unique, differentially expressed biomarkers reported. Discriminant analysis and artificial neural network predictive models displayed strong differentiating ability between glaucoma patients and controls, although these tools were untested in a clinical context. Conclusion The use of AI models could inform glaucoma diagnosis with high sensitivity and specificity. While insight into differentially expressed biomarkers is valuable in pathogenic exploration, no clear pathogenic mechanism in glaucoma has emerged.

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The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma

Cited by 8 publications

References 24 publications

Artificial intelligence in ophthalmology: an insight into neurodegenerative disease

Artificial intelligence in ophthalmology: an insight into neurodegenerative disease

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review

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