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Jan C, He M, Vingrys A, Zhu Z, Stafford RS. Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia. Eye (Lond) 2024:10.1038/s41433-024-03026-z. [PMID: 38514852 DOI: 10.1038/s41433-024-03026-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 02/05/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024] Open
Abstract
Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucial for halting progressive visual impairment in glaucoma patients, as there is no cure available. This narrative review aims to: identify reasons for the significant under-diagnosis of glaucoma globally, particularly in Australia, elucidate the role of primary healthcare in glaucoma diagnosis using Australian healthcare as an example, and discuss how recent advances in artificial intelligence (AI) can be implemented to improve diagnostic outcomes. Glaucoma is a prevalent disease in ageing populations and can have improved visual outcomes through appropriate treatment, making it essential for general medical practice. In countries such as Australia, New Zealand, Canada, USA, and the UK, optometrists serve as the gatekeepers for primary eye care, and glaucoma detection often falls on their shoulders. However, there is significant variation in the capacity for glaucoma diagnosis among eye professionals. Automation with Artificial Intelligence (AI) analysis of optic nerve photos can help optometrists identify high-risk changes and mitigate the challenges of image interpretation rapidly and consistently. Despite its potential, there are significant barriers and challenges to address before AI can be deployed in primary healthcare settings, including external validation, high quality real-world implementation, protection of privacy and cybersecurity, and medico-legal implications. Overall, the incorporation of AI technology in primary healthcare has the potential to reduce the global prevalence of undiagnosed glaucoma cases by improving diagnostic accuracy and efficiency.
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Affiliation(s)
- Catherine Jan
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia.
- Lost Child's Vision Project, Sydney, NSW, Australia.
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
- Centre for Eye and Vision Research, The Hong Kong Polytechnic University, Kowloon, TU428, Hong Kong SAR
| | - Algis Vingrys
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Randall S Stafford
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA
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AlShawabkeh M, AlRyalat SA, Al Bdour M, Alni’mat A, Al-Akhras M. The utilization of artificial intelligence in glaucoma: diagnosis versus screening. FRONTIERS IN OPHTHALMOLOGY 2024; 4:1368081. [PMID: 38984126 PMCID: PMC11182276 DOI: 10.3389/fopht.2024.1368081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 02/20/2024] [Indexed: 07/11/2024]
Abstract
With advancements in the implementation of artificial intelligence (AI) in different ophthalmology disciplines, it continues to have a significant impact on glaucoma diagnosis and screening. This article explores the distinct roles of AI in specialized ophthalmology clinics and general practice, highlighting the critical balance between sensitivity and specificity in diagnostic and screening models. Screening models prioritize sensitivity to detect potential glaucoma cases efficiently, while diagnostic models emphasize specificity to confirm disease with high accuracy. AI applications, primarily using machine learning (ML) and deep learning (DL), have been successful in detecting glaucomatous optic neuropathy from colored fundus photographs and other retinal imaging modalities. Diagnostic models integrate data extracted from various forms of modalities (including tests that assess structural optic nerve damage as well as those evaluating functional damage) to provide a more nuanced, accurate and thorough approach to diagnosing glaucoma. As AI continues to evolve, the collaboration between technology and clinical expertise should focus more on improving specificity of glaucoma diagnostic models to assess ophthalmologists to revolutionize glaucoma diagnosis and improve patients care.
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Affiliation(s)
| | - Saif Aldeen AlRyalat
- Department of Ophthalmology, The University of Jordan, Amman, Jordan
- Department of Ophthalmology, Houston Methodist Hospital, Houston, TX, United States
| | - Muawyah Al Bdour
- Department of Ophthalmology, The University of Jordan, Amman, Jordan
| | - Ayat Alni’mat
- Department of Ophthalmology, Al Taif Eye Center, Amman, Jordan
| | - Mousa Al-Akhras
- Department of Computer Information Systems, School of Information Technology and Systems, The University of Jordan, Amman, Jordan
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Yousefi S, Huang X, Poursoroush A, Majoor J, Lemij H, Vermeer K, Elze T, Wang M, Nouri-Mahdavi K, Mohammadzadeh V, Brusini P, Johnson C. An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging. OPHTHALMOLOGY SCIENCE 2024; 4:100389. [PMID: 37868793 PMCID: PMC10585627 DOI: 10.1016/j.xops.2023.100389] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 08/08/2023] [Accepted: 08/18/2023] [Indexed: 10/24/2023]
Abstract
Purpose To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements. Design Algorithm development for RNFL damage severity classification based on multicenter OCT data. Subjects and Participants A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models, and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models. Methods We developed an unsupervised k-means model to identify clusters of eyes with similar RNFL thickness profiles. We annotated the clusters based on their respective global RNFL thickness. We computed the optimal global RNFL thickness thresholds that discriminated different severity levels based on Bayes' minimum error principle. We validated the proposed pipeline based on an independent validation dataset with 2505 RNFL profiles from 1099 eyes of 900 subjects. Main Outcome Measures Accuracy, area under the receiver operating characteristic curve, and confusion matrix. Results The k-means clustering discovered 4 clusters with 1382, 1613, 1727, and 1839 samples with mean (standard deviation) global RNFL thickness of 58.3 (8.9) μm, 78.9 (6.7) μm, 87.7 (8.2) μm, and 101.5 (7.9) μm. The Bayes' minimum error classifier identified optimal global RNFL values of > 95 μ m , 86 to 95 μ m , 70 to 85 μ m , and < 70 μ m for discriminating normal eyes and eyes at the early, moderate, and advanced stages of RNFL thickness loss, respectively. About 4% of normal eyes and 98% of eyes with advanced RNFL loss had either global, or ≥ 1 quadrant, RNFL thickness outside of normal limits provided by the OCT instrument. Conclusions Unsupervised machine learning discovered that the optimal RNFL thresholds for separating normal eyes and eyes with early, moderate, and advanced RNFL loss were 95 μ m , 85 μm, and 70 μ m , respectively. This RNFL loss classification system is unbiased as there was no preassumption or human expert intervention in the development process. Additionally, it is objective, easy to use, and consistent, which may augment glaucoma research and day-to-day clinical practice. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Asma Poursoroush
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Julek Majoor
- Rotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands
| | - Hans Lemij
- Rotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands
| | - Koen Vermeer
- Rotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands
| | - Tobias Elze
- Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachussetts
| | - Mengyu Wang
- Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachussetts
| | - Kouros Nouri-Mahdavi
- Department of Ophthalmology, University of California Los Angeles, Los Angeles, California
| | - Vahid Mohammadzadeh
- Department of Ophthalmology, University of California Los Angeles, Los Angeles, California
| | - Paolo Brusini
- Department of Ophthalmology, “Città di Udine” Health Center, Udine, Italy
| | - Chris Johnson
- Department of Ophthalmology & Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa
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Huang X, Poursoroush A, Sun J, Boland MV, Johnson C, Yousefi S. Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning. ARXIV 2023:arXiv:2309.15867v1. [PMID: 37808089 PMCID: PMC10557795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Purpose To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. Design Cross-sectional and longitudinal study. Participants A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least five follow-up VF tests were included in the study. Methods We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively. Main Outcome Measure Rates of SAP mean deviation (MD) change. Results The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%) and 133 (4%). We labeled the clusters as Improvers, Stables, Slow progressors, and Fast progressors based on their mean of MD decline, which were 0.08, -0.06, -0.21, and -0.45 dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with calcium channel blockers, being male, heart disease history, diabetes history, African American race, stroke history, and migraine headaches. Conclusion Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention. Fast VF progression was associated with higher history of stroke, heart disease, diabetes, and history of more using calcium channel blockers. Fast progressors were more from African American race and males and had higher incidence of glaucoma conversion. Subtyping can provide guidance for adjusting treatment plans to slow vision loss and improve quality of life of patients with a faster progression course.
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Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Asma Poursoroush
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Jian Sun
- Integrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Bethesda, USA
| | | | - Chris Johnson
- Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, IA
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA
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Chen D, Ran Ran A, Fang Tan T, Ramachandran R, Li F, Cheung CY, Yousefi S, Tham CCY, Ting DSW, Zhang X, Al-Aswad LA. Applications of Artificial Intelligence and Deep Learning in Glaucoma. Asia Pac J Ophthalmol (Phila) 2023; 12:80-93. [PMID: 36706335 DOI: 10.1097/apo.0000000000000596] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/06/2022] [Indexed: 01/28/2023] Open
Abstract
Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.
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Affiliation(s)
- Dinah Chen
- Department of Ophthalmology, NYU Langone Health, New York City, NY
- Genentech Inc, South San Francisco, CA
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Ting Fang Tan
- Singapore Eye Research Institute, Singapore
- Singapore National Eye Center, Singapore
| | | | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Siamak Yousefi
- Department of Ophthalmology, The University of Tennessee Health Science Center, Memphis, TN
| | - Clement C Y Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore
- Singapore National Eye Center, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
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Yousefi S. Clinical Applications of Artificial Intelligence in Glaucoma. J Ophthalmic Vis Res 2023; 18:97-112. [PMID: 36937202 PMCID: PMC10020779 DOI: 10.18502/jovr.v18i1.12730] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 11/05/2022] [Indexed: 02/25/2023] Open
Abstract
Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AI-enabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice.
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Affiliation(s)
- Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
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Yousefi S, Pasquale LR, Boland MV, Johnson CA. Machine-Identified Patterns of Visual Field Loss and an Association with Rapid Progression in the Ocular Hypertension Treatment Study. Ophthalmology 2022; 129:1402-1411. [PMID: 35817199 PMCID: PMC9691587 DOI: 10.1016/j.ophtha.2022.07.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 01/06/2023] Open
Abstract
PURPOSE To identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression. DESIGN Cross-sectional and longitudinal study. PARTICIPANTS A total of 2231 abnormal VFs from 205 eyes of 176 Ocular Hypertension Treatment Study (OHTS) participants followed over approximately 16 years. METHODS Visual fields were assessed by an unsupervised deep archetypal analysis algorithm and an OHTS-certified VF reader to identify prevalent patterns of VF loss. Machine-identified patterns of glaucoma damage were compared against those patterns previously identified (expert-identified) in the OHTS in 2003. Based on the longitudinal VFs of each eye, VF loss patterns that were strongly associated with rapid glaucoma progression were identified. MAIN OUTCOME MEASURES Machine-expert correspondence and type of patterns of VF loss associated with rapid progression. RESULTS The average VF mean deviation (MD) at conversion to glaucoma was -2.7 decibels (dB) (standard deviation [SD] = 2.4 dB), whereas the average MD of the eyes at the last visit was -5.2 dB (SD = 5.5 dB). Fifty out of 205 eyes had MD rate of -1 dB/year or worse and were considered rapid progressors. Eighteen machine-identified patterns of VF loss were compared with expert-identified patterns, in which 13 patterns of VF loss were similar. The most prevalent expert-identified patterns included partial arcuate, paracentral, and nasal step defects, and the most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step, and paracentral VF defects. One of the machine-identified patterns of VF loss predicted future rapid VF progression after adjustment for age, sex, and initial MD. CONCLUSIONS An automated machine learning system can identify patterns of VF loss and could provide objective and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma.
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Affiliation(s)
- Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee; Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee.
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Michael V Boland
- Department of Ophthalmology, Massachusetts Eye and Ear, Boston, Massachusetts
| | - Chris A Johnson
- Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa
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An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence. J Glaucoma 2022; 31:626-633. [PMID: 35658070 PMCID: PMC9378471 DOI: 10.1097/ijg.0000000000002059] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 05/22/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVE The objective of this study was to develop an objective and easy-to-use glaucoma staging system based on visual fields (VFs). SUBJECTS AND PARTICIPANTS A total of 13,231 VFs from 8077 subjects were used to develop models and 8024 VFs from 4445 subjects were used to validate models. METHODS We developed an unsupervised machine learning model to identify clusters with similar VF values. We annotated the clusters based on their respective mean deviation (MD). We computed optimal MD thresholds that discriminate clusters with the highest accuracy based on Bayes minimum error principle. We evaluated the accuracy of the staging system and validated findings based on an independent validation dataset. RESULTS The unsupervised k -means algorithm discovered 4 clusters with 6784, 4034, 1541, and 872 VFs and average MDs of 0.0 dB (±1.4: SD), -4.8 dB (±1.9), -12.2 dB (±2.9), and -23.0 dB (±3.8), respectively. The supervised Bayes minimum error classifier identified optimal MD thresholds of -2.2, -8.0, and -17.3 dB for discriminating normal eyes and eyes at the early, moderate, and advanced stages of glaucoma. The accuracy of the glaucoma staging system was 94%, based on identified MD thresholds with respect to the initial k -means clusters. CONCLUSIONS We discovered that 4 severity levels based on MD thresholds of -2.2, -8.0, and -17.3 dB, provides the optimal number of severity stages based on unsupervised and supervised machine learning. This glaucoma staging system is unbiased, objective, easy-to-use, and consistent, which makes it highly suitable for use in glaucoma research and for day-to-day clinical practice.
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Gajendran MK, Rohowetz LJ, Koulen P, Mehdizadeh A. Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma. Front Neurosci 2022; 16:869137. [PMID: 35600610 PMCID: PMC9115110 DOI: 10.3389/fnins.2022.869137] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 03/28/2022] [Indexed: 01/05/2023] Open
Abstract
PurposeEarly-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.MethodsERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.ResultsRandom forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses.ConclusionsThe present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.
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Affiliation(s)
- Mohan Kumar Gajendran
- Department of Civil and Mechanical Engineering, School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Landon J. Rohowetz
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Peter Koulen
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
- Department of Biomedical Sciences, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Amirfarhang Mehdizadeh
- Department of Civil and Mechanical Engineering, School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, MO, United States
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
- *Correspondence: Amirfarhang Mehdizadeh
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Bunod R, Augstburger E, Brasnu E, Labbe A, Baudouin C. [Artificial intelligence and glaucoma: A literature review]. J Fr Ophtalmol 2022; 45:216-232. [PMID: 34991909 DOI: 10.1016/j.jfo.2021.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 11/26/2022]
Abstract
In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.
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Affiliation(s)
- R Bunod
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France.
| | - E Augstburger
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France
| | - E Brasnu
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France
| | - A Labbe
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
| | - C Baudouin
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
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Nouri-Mahdavi K, Mohammadzadeh V, Rabiolo A, Edalati K, Caprioli J, Yousefi S. Prediction of Visual Field Progression from OCT Structural Measures in Moderate to Advanced Glaucoma. Am J Ophthalmol 2021; 226:172-181. [PMID: 33529590 DOI: 10.1016/j.ajo.2021.01.023] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 01/23/2021] [Accepted: 01/25/2021] [Indexed: 01/29/2023]
Abstract
PURPOSE To test the hypothesis that visual field (VF) progression can be predicted from baseline and longitudinal optical coherence tomography (OCT) structural measurements. DESIGN Prospective cohort study. METHODS A total of 104 eyes (104 patients) with ≥3 years of follow-up and ≥5 VF examinations were enrolled. We defined VF progression based on pointwise linear regression on 24-2 VF (≥3 locations with slope less than or equal to -1.0 dB/year and P < .01). We used elastic net logistic regression (ENR) and machine learning to predict VF progression with demographics, baseline circumpapillary retinal nerve fiber layer (RNFL), macular ganglion cell/inner plexiform layer (GCIPL) thickness, and RNFL and GCIPL change rates at central 24 superpixels and 3 eccentricities, 3.4°, 5.5°, and 6.8°, from fovea and hemimaculas. Areas-under-ROC curves (AUC) were used to compare models. RESULTS Average ± SD follow-up and VF examinations were 4.5 ± 0.9 years and 8.7 ± 1.6, respectively. VF progression was detected in 23 eyes (22%). ENR selected rates of change of superotemporal RNFL sector and GCIPL change rates in 5 central superpixels and at 3.4° and 5.6° eccentricities as the best predictor subset (AUC = 0.79 ± 0.12). Best machine learning predictors consisted of baseline superior hemimacular GCIPL thickness and GCIPL change rates at 3.4° eccentricity and 3 central superpixels (AUC = 0.81 ± 0.10). Models using GCIPL-only structural variables performed better than RNFL-only models. CONCLUSIONS VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision making.
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Shuldiner SR, Boland MV, Ramulu PY, De Moraes CG, Elze T, Myers J, Pasquale L, Wellik S, Yohannan J. Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning. PLoS One 2021; 16:e0249856. [PMID: 33861775 PMCID: PMC8051770 DOI: 10.1371/journal.pone.0249856] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 03/25/2021] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test. DESIGN Retrospective analysis of longitudinal data. SUBJECTS 175,786 VFs (22,925 initial VFs) from 14,217 patients who completed ≥5 reliable VFs at academic glaucoma centers were included. METHODS Summary measures and reliability metrics from the initial VF and age were used to train MLA designed to predict the likelihood of rapid progression. Additionally, the neural network model was trained with point-wise threshold data in addition to summary measures, reliability metrics and age. 80% of eyes were used for a training set and 20% were used as a test set. MLA test set performance was assessed using the area under the receiver operating curve (AUC). Performance of models trained on initial VF data alone was compared to performance of models trained on data from the first two VFs. MAIN OUTCOME MEASURES Accuracy in predicting future rapid progression defined as MD worsening more than 1 dB/year. RESULTS 1,968 eyes (8.6%) underwent rapid progression. The support vector machine model (AUC 0.72 [95% CI 0.70-0.75]) most accurately predicted rapid progression when trained on initial VF data. Artificial neural network, random forest, logistic regression and naïve Bayes classifiers produced AUC of 0.72, 0.70, 0.69, 0.68 respectively. Models trained on data from the first two VFs performed no better than top models trained on the initial VF alone. Based on the odds ratio (OR) from logistic regression and variable importance plots from the random forest model, older age (OR: 1.41 per 10 year increment [95% CI: 1.34 to 1.08]) and higher pattern standard deviation (OR: 1.31 per 5-dB increment [95% CI: 1.18 to 1.46]) were the variables in the initial VF most strongly associated with rapid progression. CONCLUSIONS MLA can be used to predict eyes at risk for rapid progression with modest accuracy based on an initial VF test. Incorporating additional clinical data to the current model may offer opportunities to predict patients most likely to rapidly progress with even greater accuracy.
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Affiliation(s)
- Scott R. Shuldiner
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Michael V. Boland
- Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States of America
| | - Pradeep Y. Ramulu
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - C. Gustavo De Moraes
- Department of Ophthalmology, Columbia University Medical Center, New York, NY, United States of America
| | - Tobias Elze
- Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States of America
| | - Jonathan Myers
- Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, United States of America
| | - Louis Pasquale
- The Eye and Vision Research Institute of New York Eye and Ear Infirmary at Mount Sinai, Icahn School of Medicine at Mount Sinai School, New York, NY, United States of America
| | - Sarah Wellik
- Bascom Palmer Eye Institute, University of Miami, Miami, FL, United States of America
| | - Jithin Yohannan
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
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Panda BB, Thakur S, Mohapatra S, Parida S. Artificial intelligence in ophthalmology: A new era is beginning. Artif Intell Med Imaging 2021; 2:5-12. [DOI: 10.35711/aimi.v2.i1.5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 12/31/2020] [Accepted: 02/12/2021] [Indexed: 02/06/2023] Open
Abstract
The use of artificial intelligence (AI) in ophthalmology is not very new and its use is expanding into various subspecialties of the eye like retina and glaucoma, thereby helping ophthalmologists to diagnose and treat diseases better than before. Incorporating “deep learning” (a subfield of AI) into image-based systems such as optical coherence tomography has dramatically improved the machine's ability to screen and identify stages of diabetic retinopathy accurately. Similar applications have been tried in the field of retinopathy of prematurity and age-related macular degeneration, a silent retinal condition that needs to be diagnosed early to prevent progression. The advent of AI into glaucoma diagnostics in analyzing visual fields and assessing disease progression also holds a promising role. The ability of the software to detect even a subtle defect that the human eye can miss has led to a revolution in the management of certain ocular conditions. However, there are few significant challenges in the AI systems, such as the incorporation of quality images, training sets and the black box dilemma. Nevertheless, despite the existing differences, there is always a chance of improving the machines/software to potentiate their efficacy and standards. This review article shall discuss the current applications of AI in ophthalmology, significant challenges and the prospects as to how both science and medicine can work together.
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Affiliation(s)
- Bijnya Birajita Panda
- Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
| | - Subhodeep Thakur
- Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
| | - Sumita Mohapatra
- Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
| | - Subhabrata Parida
- Department ofOphthalmology, S.C.B Medical College and Hospital, Cuttack 753007, Odisha, India
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Mursch-Edlmayr AS, Ng WS, Diniz-Filho A, Sousa DC, Arnold L, Schlenker MB, Duenas-Angeles K, Keane PA, Crowston JG, Jayaram H. Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice. Transl Vis Sci Technol 2020; 9:55. [PMID: 33117612 PMCID: PMC7571273 DOI: 10.1167/tvst.9.2.55] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 09/18/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression. Methods Nonsystematic literature review using the search combinations “Artificial Intelligence,” “Deep Learning,” “Machine Learning,” “Neural Networks,” “Bayesian Networks,” “Glaucoma Diagnosis,” and “Glaucoma Progression.” Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted. Results Numerous AI strategies provide promising levels of specificity and sensitivity for structural (e.g. optical coherence tomography [OCT] imaging, fundus photography) and functional (visual field [VF] testing) test modalities used for the detection of glaucoma. Area under receiver operating curve (AROC) values of > 0.90 were achieved with every modality. Combining structural and functional inputs has been shown to even more improve the diagnostic ability. Regarding glaucoma progression, AI strategies can detect progression earlier than conventional methods or potentially from one single VF test. Conclusions AI algorithms applied to fundus photographs for screening purposes may provide good results using a simple and widely accessible test. However, for patients who are likely to have glaucoma more sophisticated methods should be used including data from OCT and perimetry. Outputs may serve as an adjunct to assist clinical decision making, whereas also enhancing the efficiency, productivity, and quality of the delivery of glaucoma care. Patients with diagnosed glaucoma may benefit from future algorithms to evaluate their risk of progression. Challenges are yet to be overcome, including the external validity of AI strategies, a move from a “black box” toward “explainable AI,” and likely regulatory hurdles. However, it is clear that AI can enhance the role of specialist clinicians and will inevitably shape the future of the delivery of glaucoma care to the next generation. Translational Relevance The promising levels of diagnostic accuracy reported by AI strategies across the modalities used in clinical practice for glaucoma detection can pave the way for the development of reliable models appropriate for their translation into clinical practice. Future incorporation of AI into healthcare models may help address the current limitations of access and timely management of patients with glaucoma across the world.
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Affiliation(s)
| | - Wai Siene Ng
- Cardiff Eye Unit, University Hospital of Wales, Cardiff, UK
| | - Alberto Diniz-Filho
- Department of Ophthalmology and Otorhinolaryngology, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - David C Sousa
- Department of Ophthalmology, Hospital de Santa Maria, Lisbon, Portugal
| | - Louis Arnold
- Department of Ophthalmology, University Hospital, Dijon, France
| | - Matthew B Schlenker
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Canada
| | - Karla Duenas-Angeles
- Department of Ophthalmology, Universidad Nacional Autónoma de Mexico, Mexico City, Mexico
| | - Pearse A Keane
- NIHR Biomedical Research Centre for Ophthalmology, UCL Institute of Ophthalmology & Moorfields Eye Hospital, London, UK
| | - Jonathan G Crowston
- Centre for Vision Research, Duke-NUS Medical School, Singapore.,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Hari Jayaram
- NIHR Biomedical Research Centre for Ophthalmology, UCL Institute of Ophthalmology & Moorfields Eye Hospital, London, UK
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15
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Pazos M, Biarnés M, Blasco-Alberto A, Dyrda A, Luque-Fernández MÁ, Gómez A, Mora C, Milla E, Muniesa M, Antón A, Díaz-Alemán VT. SD-OCT peripapillary nerve fibre layer and ganglion cell complex parameters in glaucoma: principal component analysis. Br J Ophthalmol 2020; 105:496-501. [PMID: 32493759 DOI: 10.1136/bjophthalmol-2020-316296] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 04/13/2020] [Accepted: 05/10/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND/AIMS To identify objective glaucoma-related structural features based on peripapillary (p) and macular (m) spectral domain optical coherence tomography (SD-OCT) parameters and assess their discriminative ability between healthy and glaucoma patients. METHODS Two hundred and sixty eyes (91 controls and 169 glaucoma) were included in this prospective study. After a complete examination, all participants underwent the posterior pole and the peripapillary retinal nerve fibre layer (pRNFL) protocols of the Spectralis SD-OCT. Principal component analysis (PCA), a data reduction method, was applied to identify and characterise the main information provided by the ganglion cell complex (GCC). The discriminative ability between healthy and glaucomatous eyes of the first principal components (PCs) was compared with that of conventional SD-OCT parameters (pRNFL, macular RNFL (mRNFL), macular ganglion cell layer (mGCL)and macular inner plexiform layer (mIPL)) using 10-fold cross-validated areas under the curve (AUC). RESULTS The first PC explained 58% of the total information contained in the GCC and the pRNFL parameters and was the result of a general combination of almost all variables studied (diffuse distribution). Other PCs were driven mainly by pRNFL and mRNFL measurements. PCs and pRNFL had similar AUC (0.95 vs 0.96, p=0.88), and outperformed the other structural measurements: mRNFL (0.91, p=0.002), mGCL (0.92, p=0.02) and mIPL (0.92, p=0.0001). CONCLUSIONS PCA identified a diffuse representation of the papillary and macular SD-OCT parameters as the most important PC to summarise structural data in healthy and glaucomatous eyes. PCs and pRNFL parameters showed the greatest discriminative ability between healthy and glaucoma cases.
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Affiliation(s)
- Marta Pazos
- Institut Clínic d'Oftalmologia, Hospital Clínic de Barcelona. Universitat de Barcelona, Barcelona, Spain .,Institut de la Màcula, Barcelona Macula Foundation (Hospital Quirón-Teknon), Barcelona, Spain
| | - Marc Biarnés
- Institut de la Màcula, Barcelona Macula Foundation (Hospital Quirón-Teknon), Barcelona, Spain
| | - Andrés Blasco-Alberto
- Ophthalmology, Hospital Universitario de Canarias, Universidad de la Laguna, Tenerife, Spain
| | - Agnieszka Dyrda
- Glaucoma and Research, Institut Català de Retina, Barcelona, Spain
| | - Miguel Ángel Luque-Fernández
- Non-communicable Disease and Cancer Epidemiology Group, Biomedical Research Institute of Granada (ibs.GRANADA), Granada, Spain.,Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Alicia Gómez
- Glaucoma and Research, Institut Català de Retina, Barcelona, Spain
| | - Clara Mora
- Ophthalmology, Hospital de l'Esperança-Parc de Salut Mar, Barcelona, Spain
| | - Elena Milla
- Institut Clínic d'Oftalmologia, Hospital Clínic de Barcelona. Universitat de Barcelona, Barcelona, Spain
| | - MªJesús Muniesa
- Institut Clínic d'Oftalmologia, Hospital Clínic de Barcelona. Universitat de Barcelona, Barcelona, Spain
| | - Alfonso Antón
- Glaucoma and Research, Institut Català de Retina, Barcelona, Spain.,Ophthalmology, Hospital de l'Esperança-Parc de Salut Mar, Barcelona, Spain.,Ophthalmology, Universitat Internacional de Catalunya Facultat de Medicina i Ciències de la Salut, Sant Cugat del Valles, Spain
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Thakur A, Goldbaum M, Yousefi S. Convex Representations Using Deep Archetypal Analysis for Predicting Glaucoma. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2020; 8:3800107. [PMID: 32596065 PMCID: PMC7316201 DOI: 10.1109/jtehm.2020.2982150] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/03/2020] [Accepted: 03/16/2020] [Indexed: 01/07/2023]
Abstract
Goal: The purpose of this study was to identify clinically relevant patterns of glaucomatous vision loss through convex representation to predict glaucoma several years prior to disease onset. Methods: We developed a deep archetypal analysis to identify patterns of glaucomatous vision loss, and then projected visual fields over the identified patterns. Projections provided a representation that was more accurate in detecting glaucomatous vision loss, thus, more appropriate for recognizing preclinical signs of glaucoma prior to disease development. To overcome the class imbalance in prediction, we implemented a class-balanced bagging with neural networks. Results: Using original visual field as features of the class-balanced bagging classification provided an area under the receiver-operating characteristic curve (AUC) of 0.55 for predicting glaucoma approximately four years prior to disease development. Using convex representation of the visual fields as input features provided an AUC of 0.61 while using deep convex representation as input features improved the AUC to 0.71. Relevance vector machine (RVM) achieved an AUC of 0.64. Conclusion: Deep archetypal analysis representation of visual functional features with balanced bagging classification could serve as an automated tool for predicting glaucoma. Significance: Glaucoma is the second leading cause of worldwide blindness. Most people with glaucoma have no early symptoms or pain, delaying diagnosis in many patients until they reach late irreversible vision loss stages. In fact, about 50% of people with glaucoma are unaware they have the disease. Deep archetypal analysis models may impact clinical practice in effectively identifying at-risk glaucoma patients well prior to disease development.
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Affiliation(s)
- Anshul Thakur
- School of Computing and Electrical EngineeringIndian Institute of Technology MandiMandi175005India
| | - Michael Goldbaum
- Department of OphthalmologyUniversity of California San DiegoSan DiegoCA92093USA
| | - Siamak Yousefi
- Department of OphthalmologyThe University of Tennessee Health Science CenterMemphisTN38163USA
- Department of Genetics, Genomics, and InformaticsThe University of Tennessee Health Science CenterMemphisTN38163USA
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Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard. Ophthalmology 2020; 127:1170-1178. [PMID: 32317176 DOI: 10.1016/j.ophtha.2020.03.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 01/21/2020] [Accepted: 03/03/2020] [Indexed: 01/17/2023] Open
Abstract
PURPOSE To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss. DESIGN Retrospective, cross-sectional, longitudinal cohort study. PARTICIPANTS Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models. METHOD We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma. MAIN OUTCOME MEASURES The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma. RESULTS After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%. CONCLUSIONS The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.
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18
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The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma. CURRENT OPHTHALMOLOGY REPORTS 2019. [DOI: 10.1007/s40135-019-00209-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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19
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Daien V, Muyl-Cipollina A. [Can Big Data change our practices?]. J Fr Ophtalmol 2019; 42:551-571. [PMID: 30979558 DOI: 10.1016/j.jfo.2018.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 11/22/2018] [Indexed: 11/19/2022]
Abstract
The European Medicines Agency has defined Big Data by the "3 V's": Volume, Velocity and Variety. These large databases allow access to real life data on patient care. They are particularly suited for studies of adverse events and pharmacoepidemiology. Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data using model architectures, which are composed of multiple nonlinear transformations. This article shows how Big Data and Deep Learning can help in ophthalmology, pointing out their advantages and disadvantages. A literature review is presented in this article illustrating the uses of Deep Learning in ophthalmology.
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Affiliation(s)
- V Daien
- Service d'ophtalmologique, hôpital Gui De Chauliac, 80, avenue Augustin Fliche, 34295 Montpellier, France; Inserm, epidemiological and clinical research, université Montpellier, 34295 Montpellier, France; The Save Sight Institute, Sydney Medical School, The University of Sydney, Sydney, Australie
| | - A Muyl-Cipollina
- Service d'ophtalmologique, hôpital Gui De Chauliac, 80, avenue Augustin Fliche, 34295 Montpellier, France.
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20
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Christopher M, Belghith A, Weinreb RN, Bowd C, Goldbaum MH, Saunders LJ, Medeiros FA, Zangwill LM. Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression. Invest Ophthalmol Vis Sci 2019; 59:2748-2756. [PMID: 29860461 PMCID: PMC5983908 DOI: 10.1167/iovs.17-23387] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Purpose To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Methods Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures. Results The RNFL PCA features were significantly associated with mean deviation (MD) in both SAP (R2 = 0.49, P < 0.0001) and FDT visual field testing (R2 = 0.48, P < 0.0001), and with mean circumpapillary RNFL thickness (cpRNFLt) from SD-OCT (R2 = 0.58, P < 0.0001). The identified features outperformed each of these measures in detecting glaucoma with an AUC of 0.95 for RNFL PCA compared to an 0.90 for mean cpRNFLt (P = 0.09), 0.86 for SAP MD (P = 0.034), and 0.83 for FDT MD (P = 0.021). Accuracy in predicting progression was also significantly higher for RNFL PCA compared to SAP MD, FDT MD, and mean cpRNFLt (P = 0.046, P = 0.007, and P = 0.044, respectively). Conclusions A computational approach can identify structural features that improve glaucoma detection and progression prediction.
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Affiliation(s)
- Mark Christopher
- Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
| | - Akram Belghith
- Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
| | - Robert N Weinreb
- Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
| | - Christopher Bowd
- Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
| | - Michael H Goldbaum
- Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
| | - Luke J Saunders
- Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
| | - Felipe A Medeiros
- Duke Eye Center, Department of Ophthalmology, Duke University, Durham, North Carolina, United States
| | - Linda M Zangwill
- Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
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Abstract
PURPOSE OF REVIEW The use of computers has become increasingly relevant to medical decision-making, and artificial intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current artificial intelligence methods and their applications, to help the practicing ophthalmologist understand their potential impact on glaucoma care. RECENT FINDINGS Techniques used in artificial intelligence can successfully analyze and categorize data from visual fields, optic nerve structure [e.g., optical coherence tomography (OCT) and fundus photography], ocular biomechanical properties, and a combination thereof to identify disease severity, determine disease progression, and/or recommend referral for specialized care. Algorithms have become increasingly complex in recent years, utilizing both supervised and unsupervised methods of artificial intelligence. Impressive performance of these algorithms on previously unseen data has been reported, often outperforming standard global indices and expert observers. However, there remains no clearly defined gold standard for determining the presence and severity of glaucoma, which undermines the training of these algorithms. To improve upon existing methodologies, future work must employ more robust definitions of disease, optimize data inputs for artificial intelligence analysis, and improve methods of extracting knowledge from learned results. SUMMARY Artificial intelligence has the potential to revolutionize the screening, diagnosis, and classification of glaucoma, both through the automated processing of large data sets, and by earlier detection of new disease patterns. In addition, artificial intelligence holds promise for fundamentally changing research aimed at understanding the development, progression, and treatment of glaucoma, by identifying novel risk factors and by evaluating the importance of existing ones.
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22
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Akkara J, Kuriakose A. Role of artificial intelligence and machine learning in ophthalmology. KERALA JOURNAL OF OPHTHALMOLOGY 2019. [DOI: 10.4103/kjo.kjo_54_19] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Yousefi S, Kiwaki T, Zheng Y, Sugiura H, Asaoka R, Murata H, Lemij H, Yamanishi K. Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning. Am J Ophthalmol 2018; 193:71-79. [PMID: 29920226 DOI: 10.1016/j.ajo.2018.06.007] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 06/05/2018] [Accepted: 06/06/2018] [Indexed: 10/28/2022]
Abstract
PURPOSE Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine learning-based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices. DESIGN Development and comparison of a prognostic index. METHOD Visual fields from 2085 eyes of 1214 subjects were used to identify glaucoma progression patterns using machine learning. Visual fields from 133 eyes of 71 glaucoma patients were collected 10 times over 10 weeks to provide a no-change, test-retest dataset. The parameters of all methods were identified using visual field sequences in the test-retest dataset to meet fixed 95% specificity. An independent dataset of 270 eyes of 136 glaucoma patients and survival analysis were used to compare methods. RESULTS The time to detect progression in 25% of the eyes in the longitudinal dataset using global mean deviation (MD) was 5.2 (95% confidence interval, 4.1-6.5) years; 4.5 (4.0-5.5) years using region-wise, 3.9 (3.5-4.6) years using point-wise, and 3.5 (3.1-4.0) years using machine learning analysis. The time until 25% of eyes showed subsequently confirmed progression after 2 additional visits were included were 6.6 (5.6-7.4) years, 5.7 (4.8-6.7) years, 5.6 (4.7-6.5) years, and 5.1 (4.5-6.0) years for global, region-wise, point-wise, and machine learning analyses, respectively. CONCLUSIONS Machine learning analysis detects progressing eyes earlier than other methods consistently, with or without confirmation visits. In particular, machine learning detects more slowly progressing eyes than other methods.
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Abstract
This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.
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Affiliation(s)
- Miguel Caixinha
- a Department of Physics, Faculty of Sciences and Technology , University of Coimbra , Coimbra , Portugal.,b Department of Electrical and Computer Engineering, Faculty of Sciences and Technology , University of Coimbra , Coimbra , Portugal
| | - Sandrina Nunes
- c Faculty of Medicine, University of Coimbra , Coimbra , Portugal.,d Coimbra Coordinating Centre for Clinical Research, Association for Innovation and Biomedical Research on Light and Image , Coimbra , Portugal
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Yousefi S, Balasubramanian M, Goldbaum MH, Medeiros FA, Zangwill LM, Weinreb RN, Liebmann JM, Girkin CA, Bowd C. Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields. Transl Vis Sci Technol 2016; 5:2. [PMID: 27152250 PMCID: PMC4855479 DOI: 10.1167/tvst.5.3.2] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 03/06/2016] [Indexed: 11/24/2022] Open
Abstract
Purpose To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM–progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. Methods GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI). Results Sensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI. Conclusions GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information. Translational Relevance Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.
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Affiliation(s)
- Siamak Yousefi
- Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA
| | - Madhusudhanan Balasubramanian
- Department of Electrical and Computer Engineering; Department of Biomedical Engineering, University of Memphis, Memphis, TN, USA
| | - Michael H Goldbaum
- Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA
| | - Felipe A Medeiros
- Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA
| | - Linda M Zangwill
- Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA
| | - Robert N Weinreb
- Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA
| | | | | | - Christopher Bowd
- Hamilton Glaucoma Center and the Department of Ophthalmology University of California San Diego, La Jolla, CA, USA
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Yousefi S, Goldbaum MH, Varnousfaderani ES, Belghith A, Jung TP, Medeiros FA, Zangwill LM, Weinreb RN, Liebmann JM, Girkin CA, Bowd C. Detecting glaucomatous change in visual fields: Analysis with an optimization framework. J Biomed Inform 2015; 58:96-103. [PMID: 26440445 DOI: 10.1016/j.jbi.2015.09.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 09/15/2015] [Accepted: 09/27/2015] [Indexed: 11/16/2022]
Abstract
Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants.
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Affiliation(s)
- Siamak Yousefi
- Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
| | - Michael H Goldbaum
- Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
| | - Ehsan S Varnousfaderani
- Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
| | - Akram Belghith
- Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
| | - Tzyy-Ping Jung
- Institute for Neural Computation and Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA
| | - Felipe A Medeiros
- Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
| | - Linda M Zangwill
- Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
| | - Robert N Weinreb
- Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA
| | | | | | - Christopher Bowd
- Hamilton Glaucoma Center and the Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA.
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Yousefi S, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Liebmann JM, Girkin CA, Weinreb RN, Bowd C. Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements. IEEE Trans Biomed Eng 2014; 61:2112-24. [PMID: 24710816 DOI: 10.1109/tbme.2014.2314714] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.
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Yousefi S, Goldbaum MH, Zangwill LM, Medeiros FA, Bowd C. Recognizing patterns of visual field loss using unsupervised machine learning. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 2014:90342M. [PMID: 25593676 PMCID: PMC4292883 DOI: 10.1117/12.2043145] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Glaucoma is a potentially blinding optic neuropathy that results in a decrease in visual sensitivity. Visual field abnormalities (decreased visual sensitivity on psychophysical tests) are the primary means of glaucoma diagnosis. One form of visual field testing is Frequency Doubling Technology (FDT) that tests sensitivity at 52 points within the visual field. Like other psychophysical tests used in clinical practice, FDT results yield specific patterns of defect indicative of the disease. We used Gaussian Mixture Model with Expectation Maximization (GEM), (EM is used to estimate the model parameters) to automatically separate FDT data into clusters of normal and abnormal eyes. Principal component analysis (PCA) was used to decompose each cluster into different axes (patterns). FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal (i.e., glaucomatous) FDT results, recruited from a university-based, longitudinal, multi-center, clinical study on glaucoma. The GEM input was the 52-point FDT threshold sensitivities for all eyes. The optimal GEM model separated the FDT fields into 3 clusters. Cluster 1 contained 94% normal fields (94% specificity) and clusters 2 and 3 combined, contained 77% abnormal fields (77% sensitivity). For clusters 1, 2 and 3 the optimal number of PCA-identified axes were 2, 2 and 5, respectively. GEM with PCA successfully separated FDT fields from healthy and glaucoma eyes and identified familiar glaucomatous patterns of loss.
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Affiliation(s)
- Siamak Yousefi
- Hamilton Glaucoma Center, Ophthalmology Department, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, USA 92093
| | - Michael H. Goldbaum
- Hamilton Glaucoma Center, Ophthalmology Department, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, USA 92093
| | - Linda M. Zangwill
- Hamilton Glaucoma Center, Ophthalmology Department, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, USA 92093
| | - Felipe A. Medeiros
- Hamilton Glaucoma Center, Ophthalmology Department, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, USA 92093
| | - Christopher Bowd
- Hamilton Glaucoma Center, Ophthalmology Department, University of California San Diego, 9415 Campus Point Dr, La Jolla, CA, USA 92093
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