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Verticchio Vercellin A, Harris A, Oddone F, Carnevale C, Siesky BA, Arciero J, Fry B, Eckert G, Sidoti PA, Antman G, Alabi D, Coleman-Belin JC, Pasquale LR. Diagnostic Capability of OCTA-Derived Macular Biomarkers for Early to Moderate Primary Open Angle Glaucoma. J Clin Med 2024; 13:4190. [PMID: 39064230 PMCID: PMC11278250 DOI: 10.3390/jcm13144190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
Background/Objectives: To investigate macular vascular biomarkers for the detection of primary open-angle glaucoma (POAG). Methods: A total of 56 POAG patients and 94 non-glaucomatous controls underwent optical coherence tomography angiography (OCTA) assessment of macular vessel density (VD) in the superficial (SCP), and deep (DCP) capillary plexus, foveal avascular zone (FAZ) area, perimeter, VD, choriocapillaris and outer retina flow area. POAG patients were classified for severity based on the Glaucoma Staging System 2 of Brusini. ANCOVA comparisons adjusted for age, sex, race, hypertension, diabetes, and areas under the receiver operating characteristic curves (AUCs) for POAG/control differentiation were compared using the DeLong method. Results: Global, hemispheric, and quadrant SCP VD was significantly lower in POAG patients in the whole image, parafovea, and perifovea (p < 0.001). No significant differences were found between POAG and controls for DCP VD, FAZ parameters, and the retinal and choriocapillaris flow area (p > 0.05). SCP VD in the whole image and perifovea were significantly lower in POAG patients in stage 2 than stage 0 (p < 0.001). The AUCs of SCP VD in the whole image (0.86) and perifovea (0.84) were significantly higher than the AUCs of all DCP VD (p < 0.05), FAZ parameters (p < 0.001), and retinal (p < 0.001) and choriocapillaris flow areas (p < 0.05). Whole image SCP VD was similar to the AUC of the global retinal nerve fiber layer (RNFL) (AUC = 0.89, p = 0.53) and ganglion cell complex (GCC) thickness (AUC = 0.83, p = 0.42). Conclusions: SCP VD is lower with increasing functional damage in POAG patients. The AUC for SCP VD was similar to RNFL and GCC using clinical diagnosis as the reference standard.
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Affiliation(s)
- Alice Verticchio Vercellin
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.V.V.); (B.A.S.); (P.A.S.); (G.A.); (D.A.); (J.C.C.-B.); (L.R.P.)
| | - Alon Harris
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.V.V.); (B.A.S.); (P.A.S.); (G.A.); (D.A.); (J.C.C.-B.); (L.R.P.)
| | - Francesco Oddone
- Glaucoma Unit, IRCCS—Fondazione Bietti, 00198 Rome, Italy; (F.O.); (C.C.)
| | - Carmela Carnevale
- Glaucoma Unit, IRCCS—Fondazione Bietti, 00198 Rome, Italy; (F.O.); (C.C.)
| | - Brent A. Siesky
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.V.V.); (B.A.S.); (P.A.S.); (G.A.); (D.A.); (J.C.C.-B.); (L.R.P.)
| | - Julia Arciero
- Department of Mathematical Sciences, Indiana University Indianapolis, Indianapolis, IN 46202, USA;
| | - Brendan Fry
- Department of Mathematics and Statistics, Metropolitan State University of Denver, Denver, CO 80204, USA;
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Paul A. Sidoti
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.V.V.); (B.A.S.); (P.A.S.); (G.A.); (D.A.); (J.C.C.-B.); (L.R.P.)
- New York Eye and Ear Infirmary of Mount Sinai, New York, NY 10003, USA
| | - Gal Antman
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.V.V.); (B.A.S.); (P.A.S.); (G.A.); (D.A.); (J.C.C.-B.); (L.R.P.)
- Department of Ophthalmology, Rabin Medical Center, Petach Tikwa 4941492, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Denise Alabi
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.V.V.); (B.A.S.); (P.A.S.); (G.A.); (D.A.); (J.C.C.-B.); (L.R.P.)
| | - Janet C. Coleman-Belin
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.V.V.); (B.A.S.); (P.A.S.); (G.A.); (D.A.); (J.C.C.-B.); (L.R.P.)
| | - Louis R. Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (A.V.V.); (B.A.S.); (P.A.S.); (G.A.); (D.A.); (J.C.C.-B.); (L.R.P.)
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Kenney RC, Requarth TW, Jack AI, Hyman SW, Galetta SL, Grossman SN. AI in Neuro-Ophthalmology: Current Practice and Future Opportunities. J Neuroophthalmol 2024:00041327-990000000-00679. [PMID: 38965655 DOI: 10.1097/wno.0000000000002205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2024]
Abstract
BACKGROUND Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease. EVIDENCE ACQUISITION Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI. RESULTS This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice. CONCLUSIONS We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.
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Affiliation(s)
- Rachel C Kenney
- Departments of Neurology (RCK, AJ, SH, SG, SNG), Population Health (RCK), and Ophthalmology (SG), New York University Grossman School of Medicine, New York, New York; and Vilcek Institute of Graduate Biomedical Sciences (TR), New York University Grossman School of Medicine, New York, New York
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3
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Hong J, Tan SS, Chua J. Optical coherence tomography angiography in glaucoma. Clin Exp Optom 2024; 107:110-121. [PMID: 38266148 DOI: 10.1080/08164622.2024.2306963] [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: 10/24/2023] [Accepted: 01/11/2024] [Indexed: 01/26/2024] Open
Abstract
The use of optical coherence tomography angiography (OCTA) holds significant promise for optometrists in the diagnosis and management of glaucoma. It offers reliable differentiation of glaucomatous eyes from healthy ones and extends monitoring capabilities for advanced cases. OCTA represents a valuable addition to traditional assessment methods, particularly in complex cases. Glaucoma, a major cause of irreversible blindness, is traditionally diagnosed using structural and functional metrics. With growing interest, OCTA is being explored to diagnose, monitor, and manage glaucoma. This review focuses on the application of OCTA in glaucoma patients. A database search was carried out using Embase Elsevier (n = 664), PubMed (n = 574), and Cochrane Central Register of Controlled Trials (n = 19) on 15 August 2023. After deduplication and screening, 272 original papers were included in the narrative review. Inclusion criteria comprised English-language original studies on OCTA use in human glaucoma patients, with or without healthy controls. Exclusion criteria encompassed animal studies, in-vivo/in-vitro research, reviews, and congress abstracts. OCTA has good repeatability and reproducibility. OCTA metrics have good discriminatory power to differentiate glaucomatous eyes from healthy eyes and show strong associations with structural changes and visual field defects. OCTA can extend the monitoring of advanced glaucoma, addressing the 'floor effect' of traditional structural measurements. OCTA metrics can be affected by the choice of OCTA machine, post-image processing algorithms, systemic diseases, and ocular factors. Image artefacts can affect the accuracy of OCTA measurements, and proper scan quality evaluation is crucial to ensure reliable results. Additionally, artificial intelligence techniques offer promise for enhancing the diagnostic accuracy of OCTA by combining data from various retinal layers and regions. OCTA complements traditional methods in assessing glaucoma, especially in challenging cases, providing valuable insights for detection and management. Further research and clinical validation are needed to integrate OCTA into routine practice.
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Affiliation(s)
- Jimmy Hong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Shayne S Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Jacqueline Chua
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
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Correia Barão R, Hemelings R, Abegão Pinto L, Pazos M, Stalmans I. Artificial intelligence for glaucoma: state of the art and future perspectives. Curr Opin Ophthalmol 2024; 35:104-110. [PMID: 38018807 DOI: 10.1097/icu.0000000000001022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
PURPOSE OF REVIEW To address the current role of artificial intelligence (AI) in the field of glaucoma. RECENT FINDINGS Current deep learning (DL) models concerning glaucoma diagnosis have shown consistently improving diagnostic capabilities, primarily based on color fundus photography and optical coherence tomography, but also with multimodal strategies. Recent models have also suggested that AI may be helpful in detecting and estimating visual field progression from different input data. Moreover, with the emergence of newer DL architectures and synthetic data, challenges such as model generalizability and explainability have begun to be tackled. SUMMARY While some challenges remain before AI is routinely employed in clinical practice, new research has expanded the range in which it can be used in the context of glaucoma management and underlined the relevance of this research avenue.
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Affiliation(s)
- Rafael Correia Barão
- Department of Ophthalmology, Hospital de Santa Maria, CHULN
- Visual Sciences Study Center, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Ruben Hemelings
- Department of Neurosciences, Research Group Ophthalmology, KU Leuven, Leuven, Belgium
- Singapore Eye Research Institute, Singapore National Eye Centre
- SERI-NTU Advanced Ocular Engineering (STANCE) Programme, Singapore, Singapore
| | - Luís Abegão Pinto
- Department of Ophthalmology, Hospital de Santa Maria, CHULN
- Visual Sciences Study Center, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Marta Pazos
- Institute of Ophthalmology, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Ingeborg Stalmans
- Department of Neurosciences, Research Group Ophthalmology, KU Leuven, Leuven, Belgium
- Department of Ophthalmology, University Hospitals UZ Leuven, Leuven, Belgium
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Jalili J, Nadimi M, Jafari B, Esfandiari A, Mojarad M, Subramanian PS, Aghsaei Fard M. Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Optic Neuropathies Using Machine Learning. J Neuroophthalmol 2024; 44:41-46. [PMID: 37440373 DOI: 10.1097/wno.0000000000001925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2023]
Abstract
BACKGROUND To evaluate the classification performance of machine learning based on the 4 vessel density features of peripapillary optical coherence tomography angiography (OCT-A) for classifying healthy, nonarteritic anterior ischemic optic neuropathy (NAION), and optic neuritis (ON) eyes. METHODS Forty-five eyes of 45 NAION patients, 32 eyes of 32 ON patients, and 76 eyes of 76 healthy individuals with optic nerve head OCT-A were included. Four vessel density features of OCT-A images were developed using a threshold-based segmentation method and were integrated in 3 models of machine learning classifiers. Classification performances of support vector machine (SVM), random forest, and Gaussian Naive Bayes (GNB) models were evaluated with the area under the receiver-operating-characteristic curve (AUC) and accuracy. RESULTS We divided 121 images into a 70% training set and 30% test set. For ON-NAION classification, best results were achieved with 50% threshold, in which 3 classifiers (SVM, RF, and GNB) discriminated ON from NAION with an AUC of 1 and accuracy of 1. For ON-Normal classification, with 100% threshold, SVM and RF classifiers were able to discriminate normal from ON with AUCs of 1 and accuracies of 1. For NAION-normal classification, with 50% threshold, the SVM and RF classified the NAION from normal with AUC and accuracy of 1. CONCLUSIONS ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for NAION and ON distinction.
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Affiliation(s)
- Jalil Jalili
- Biomedical Engineering Unit (JJ, MN), Cardiovascular Disease Research Center, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran; Farabi Eye Hospital (BJ, AE, MAF), Tehran University of Medical Sciences, Tehran, Iran; School of Medicine (MM), Guilan University of Medical Sciences, Rasht, Iran; and Department of Ophthalmology (PSS), University of Colorado, School of Medicine, Aurora, Colorado
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Mastropasqua L, Agnifili L, Brescia L, Figus M, Posarelli C, Oddone F, Giammaria S, Sacchi M, Pavan M, Innocenti DD, Olivotto V, Sensi SL, Mastropasqua R. A deep learning approach to investigate the filtration bleb functionality after glaucoma surgery: a preliminary study. Graefes Arch Clin Exp Ophthalmol 2024; 262:149-160. [PMID: 37530849 PMCID: PMC10805808 DOI: 10.1007/s00417-023-06170-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 06/30/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023] Open
Abstract
PURPOSE To distinguish functioning from failed filtration blebs (FBs) implementing a deep learning (DL) model on slit-lamp images. METHODS Retrospective, cross-sectional, multicenter study for development and validation of an artificial intelligence classification algorithm. The dataset consisted of 119 post-trabeculectomy FB images of whom we were aware of the surgical outcome. The ground truth labels were annotated and images splitted into three outcome classes: complete (C) or qualified success (Q), and failure (F). Images were prepared implementing various data cleaning and data transformations techniques. A set of DL models were trained using different ResNet architectures as the backbone. Transfer and ensemble learning were then applied to obtain a final combined model. Accuracy, sensitivity, specificity, area under the ROC curve, and area under the precision-recall curve were calculated to evaluate the final model. Kappa coefficient and P value on the accuracy measure were used to prove the statistical significance level. RESULTS The DL approach reached good results in unraveling FB functionality. Overall, the model accuracy reached a score of 74%, with a sensitivity of 74% and a specificity of 87%. The area under the ROC curve was 0.8, whereas the area under the precision-recall curve was 0.74. The P value was equal to 0.00307, and the Kappa coefficient was 0.58. CONCLUSIONS All considered metrics supported that the final DL model was able to discriminate functioning from failed FBs, with good accuracy. This approach could support clinicians in the patients' management after glaucoma surgery in absence of adjunctive clinical data.
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Affiliation(s)
- Leonardo Mastropasqua
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy
| | - Luca Agnifili
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy.
| | - Lorenza Brescia
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy
| | - Michele Figus
- Ophthalmology Unit, Department of Surgical, Medical, Molecular Pathology and Critical Care Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Chiara Posarelli
- Ophthalmology Unit, Department of Surgical, Medical, Molecular Pathology and Critical Care Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | | | - Sara Giammaria
- IRCCS Fondazione Bietti, Via Livenza, 3, 00198, Rome, Italy
| | - Matteo Sacchi
- University Eye Clinic, San Giuseppe Hospital, University of Milan, Milan, Italy
| | - Marco Pavan
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Dante Degli Innocenti
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Valentina Olivotto
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Stefano L Sensi
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), "G. d'Annunzio" University of Chieti-Pescara, Via Dei Vestini 31, 66100, Chieti, Italy
| | - Rodolfo Mastropasqua
- Department of Neuroscience, Imaging and Clinical Sciences (DNISC), "G. d'Annunzio" University of Chieti-Pescara, Via Dei Vestini 31, 66100, Chieti, Italy
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Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - 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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Jalili J, Nadimi M, Jafari B, Esfandiari A, Sadeghi R, Ghahari P, Sajedi M, Safizade M, Aghsaei Fard M. Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Glaucoma Using Machine Learning. J Glaucoma 2023; 32:1006-1010. [PMID: 37974327 DOI: 10.1097/ijg.0000000000002329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/10/2023] [Indexed: 11/19/2023]
Abstract
PRCIS Machine learning (ML) based on the optical coherence tomography angiography vessel density features with different thresholds using a support vector machine (SVM) model provides excellent performance for glaucoma detection. BACKGROUND To assess the classification performance of ML based on the 4 vessel density features of peripapillary optical coherence tomography angiography for glaucoma detection. METHODS Images from 119 eyes of 119 glaucoma patients and 76 eyes of 76 healthy individuals were included. Four vessel density features of optical coherence tomography angiography images were developed using a threshold-based segmentation method and were integrated into 3 models of machine learning classifiers. Images were divided into 70% training set and 30% test set. Classification performances of SVM, random forest, and Gaussian Naive Bayes models were evaluated with the area under the receiver operating characteristic curve (AUC) and accuracy. RESULTS Glaucoma eyes had lower vessel densities at different thresholds. For differentiating glaucoma eyes, the best results were achieved with 70% and 100% thresholds, in which SVM classifier discriminated glaucoma from healthy eyes with an AUC of 1 and accuracy of 1. After SVM, the random forest classifier with 100% thresholds showed an AUC of 0.993 and an accuracy of 0.994. Furthermore, the AUC of our ML performance (SVM) was 0.96 in a subgroup analysis of mild and moderate glaucoma eyes. CONCLUSIONS ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for glaucoma detection.
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Affiliation(s)
- Jalil Jalili
- Biomedical Engineering Unit, Cardiovascular Disease Research Center, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht
| | - Mohadeseh Nadimi
- Biomedical Engineering Unit, Cardiovascular Disease Research Center, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht
| | - Behzad Jafari
- Farabi Eye Hospital, Tehran University of Medical Sciences
| | | | - Reza Sadeghi
- Farabi Eye Hospital, Tehran University of Medical Sciences
| | - Parichehr Ghahari
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mona Safizade
- Farabi Eye Hospital, Tehran University of Medical Sciences
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Hormel TT, Jia Y. OCT angiography and its retinal biomarkers [Invited]. BIOMEDICAL OPTICS EXPRESS 2023; 14:4542-4566. [PMID: 37791289 PMCID: PMC10545210 DOI: 10.1364/boe.495627] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 10/05/2023]
Abstract
Optical coherence tomography angiography (OCTA) is a high-resolution, depth-resolved imaging modality with important applications in ophthalmic practice. An extension of structural OCT, OCTA enables non-invasive, high-contrast imaging of retinal and choroidal vasculature that are amenable to quantification. As such, OCTA offers the capability to identify and characterize biomarkers important for clinical practice and therapeutic research. Here, we review new methods for analyzing biomarkers and discuss new insights provided by OCTA.
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Affiliation(s)
- Tristan T. Hormel
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Yali Jia
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
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Sherif EM, Matter RM, Salah NY, Abozeid NEH, Atif HM, Tantawy NM. Changes in early optical coherence tomography angiography among children and adolescents with type 1 diabetes: Relation to fibroblast growth factor 21. Diabetes Metab Res Rev 2023; 39:e3598. [PMID: 36494875 DOI: 10.1002/dmrr.3598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 11/08/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022]
Abstract
AIMS Current diagnostic and treatment modalities target late stages of diabetic retinopathy (DR) when retinopathy has already been established. Novel and more sensitive strategies are needed. Optical coherence tomography angiography (OCTA) permits non-invasive visualisation of retinal microcirculation. Fibroblast growth factor-21 (FGF21) plays an important role in glucose and lipid homoeostasis. This study assesses early OCTA changes among children and adolescents with type 1 diabetes (T1DM) compared to fundus photography and correlates them to diabetes-duration, glycaemic control, and FGF21; hence, it determines their value in early detection of DR. METHODOLOGY Hundred children and adolescents with T1DM were assessed for diabetes-duration, insulin therapy, hypoglycemia, and diabetic-ketoacidosis frequency, Tanner staging, glycated-haemoglobin (HbA1c), fasting lipids, urinary albumin/creatinine ratio, and serum FGF21. OCTA and fundus photography were done for the studied patients and 100 age, gender, and Tanner matched healthy controls. RESULTS The mean age of the children and adolescents with T1DM was 10.84 years, their mean diabetes-duration was 3.27 years and their median FGF21 was 150 pg/ml. FGF21 was significantly higher among children and adolescents with T1DM than controls (p < 0.001). Children and adolescents with T1DM had a significantly larger foveal avascular zone (FAZ) and lower peripapillary and inside-disc capillary densities (p < 0.05); with no significant fundus photography difference (p = 0.155) than controls. FAZ was positively correlated and peripapillary and inside-disc capillary densities were negatively correlated with diabetes-duration, HbA1c, FGF21, and Tanner stage. FGF21 was significantly higher in T1DM children and adolescents having OCTA changes compared to those with normal OCTA (p = 0.002). Multivariate-regression revealed that FAZ is independently associated with diabetes-duration, HbA1c and FGF21. CONCLUSIONS OCTA changes start early in children and adolescents with T1DM long before the fundus changes. These changes are correlated with diabetes-duration, puberty, glycaemic, and FGF21.
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Affiliation(s)
- Eman M Sherif
- Pediatrics and Adolescents Diabetes Unit, Pediatrics Department, Faculty of Medicine, Ain-Shams University, Cairo, Egypt
| | - Randa M Matter
- Pediatrics and Adolescents Diabetes Unit, Pediatrics Department, Faculty of Medicine, Ain-Shams University, Cairo, Egypt
| | - Nouran Yousef Salah
- Pediatrics and Adolescents Diabetes Unit, Pediatrics Department, Faculty of Medicine, Ain-Shams University, Cairo, Egypt
| | - Nour Eldin H Abozeid
- Opthalmology Department, Faculty of Medicine, Ain-Shams University, Cairo, Egypt
| | - Heba M Atif
- Clinical Pathology Department, Faculty of Medicine, Ain-Shams University, Cairo, Egypt
| | - Nermien M Tantawy
- Pediatrics and Adolescents Diabetes Unit, Pediatrics Department, Faculty of Medicine, Ain-Shams University, Cairo, Egypt
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Kamalipour A, Moghimi S, Khosravi P, Mohammadzadeh V, Nishida T, Micheletti E, Wu JH, Mahmoudinezhad G, Li EHF, Christopher M, Zangwill L, Javidi T, Weinreb RN. Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma. Am J Ophthalmol 2023; 246:141-154. [PMID: 36328200 DOI: 10.1016/j.ajo.2022.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE To use longitudinal optical coherence tomography (OCT) and OCT angiography (OCTA) data to detect glaucomatous visual field (VF) progression with a supervised machine learning approach. DESIGN Prospective cohort study. METHODS One hundred ten eyes of patients with suspected glaucoma (33.6%) and patients with glaucoma (66.4%) with a minimum of 5 24-2 VF tests and 3 optic nerve head and macula images over an average follow-up duration of 4.1 years were included. VF progression was defined using a composite measure including either a "likely progression event" on Guided Progression Analysis, a statistically significant negative slope of VF mean deviation or VF index, or a positive pointwise linear regression event. Feature-based gradient boosting classifiers were developed using different subsets of baseline and longitudinal OCT and OCTA summary parameters. The area under the receiver operating characteristic curve (AUROC) was used to compare the classification performance of different models. RESULTS VF progression was detected in 28 eyes (25.5%). The model with combined baseline and longitudinal OCT and OCTA parameters at the global and hemifield levels had the best classification accuracy to detect VF progression (AUROC = 0.89). Models including combined OCT and OCTA parameters had higher classification accuracy compared with those with individual subsets of OCT or OCTA features alone. Including hemifield measurements significantly improved the models' classification accuracy compared with using global measurements alone. Including longitudinal rates of change of OCT and OCTA parameters (AUROCs = 0.80-0.89) considerably increased the classification accuracy of the models with baseline measurements alone (AUROCs = 0.60-0.63). CONCLUSIONS Longitudinal OCTA measurements complement OCT-derived structural metrics for the evaluation of functional VF loss in patients with glaucoma.
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Affiliation(s)
- Alireza Kamalipour
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Sasan Moghimi
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Pooya Khosravi
- School of Medicine (P.K.), University of California, Irvine, Irvine, California, USA
| | - Vahid Mohammadzadeh
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Takashi Nishida
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Eleonora Micheletti
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Jo-Hsuan Wu
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Golnoush Mahmoudinezhad
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Elizabeth H F Li
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Mark Christopher
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Linda Zangwill
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Tara Javidi
- Department of Electrical and Computer Engineering (T.J.), University of California San Diego, La Jolla
| | - Robert N Weinreb
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology.
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12
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Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13020326. [PMID: 36673135 PMCID: PMC9857993 DOI: 10.3390/diagnostics13020326] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the "proof-of-concept" stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions.
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13
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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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14
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Thompson AC, Falconi A, Sappington RM. Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging. FRONTIERS IN OPHTHALMOLOGY 2022; 2:937205. [PMID: 38983522 PMCID: PMC11182271 DOI: 10.3389/fopht.2022.937205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/22/2022] [Indexed: 07/11/2024]
Abstract
Glaucoma is a leading cause of progressive blindness and visual impairment worldwide. Microstructural evidence of glaucomatous damage to the optic nerve head and associated tissues can be visualized using optical coherence tomography (OCT). In recent years, development of novel deep learning (DL) algorithms has led to innovative advances and improvements in automated detection of glaucomatous damage and progression on OCT imaging. DL algorithms have also been trained utilizing OCT data to improve detection of glaucomatous damage on fundus photography, thus improving the potential utility of color photos which can be more easily collected in a wider range of clinical and screening settings. This review highlights ten years of contributions to glaucoma detection through advances in deep learning models trained utilizing OCT structural data and posits future directions for translation of these discoveries into the field of aging and the basic sciences.
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Affiliation(s)
- Atalie C. Thompson
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston Salem, NC, United States
- Department of Internal Medicine, Gerontology, and Geriatric Medicine, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Aurelio Falconi
- Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Rebecca M. Sappington
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston Salem, NC, United States
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC, United States
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15
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Charng J, Alam K, Swartz G, Kugelman J, Alonso-Caneiro D, Mackey DA, Chen FK. Deep learning: applications in retinal and optic nerve diseases. Clin Exp Optom 2022:1-10. [PMID: 35999058 DOI: 10.1080/08164622.2022.2111201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
Abstract
Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.
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Affiliation(s)
- Jason Charng
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Khyber Alam
- Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Gavin Swartz
- Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Jason Kugelman
- School of Optometry and Vision Science, Queensland University of Technology, Brisbane, Australia
| | - David Alonso-Caneiro
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,School of Optometry and Vision Science, Queensland University of Technology, Brisbane, Australia
| | - David A Mackey
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia.,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Fred K Chen
- Centre of Ophthalmology and Visual Science (incorporating Lions Eye Institute), University of Western Australia, Perth, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia.,Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.,Department of Ophthalmology, Royal Perth Hospital, Western Australia, Perth, Australia
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16
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Yi S, Zhang G, Qian C, Lu Y, Zhong H, He J. A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning. Front Neurosci 2022; 16:939472. [PMID: 35844230 PMCID: PMC9277547 DOI: 10.3389/fnins.2022.939472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
Glaucoma is an optic neuropathy that leads to characteristic visual field defects. However, there is no cure for glaucoma, so the diagnosis of its severity is essential for its prevention. In this paper, we propose a multimodal classification architecture based on deep learning for the severity diagnosis of glaucoma. In this architecture, a gray scale image of the visual field is first reconstructed with a higher resolution in the preprocessing stage, and more subtle feature information is provided for glaucoma diagnosis. We then use multimodal fusion technology to integrate fundus images and gray scale images of the visual field as the input of this architecture. Finally, the inherent limitation of convolutional neural networks (CNNs) is addressed by replacing the original classifier with the proposed classifier. Our architecture is trained and tested on the datasets provided by the First Affiliated Hospital of Kunming Medical University, and the results show that the proposed architecture achieves superior performance for glaucoma diagnosis.
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Affiliation(s)
- Sanli Yi
- School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Gang Zhang
- School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Chaoxu Qian
- First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - YunQing Lu
- First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Hua Zhong
- First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jianfeng He
- School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
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17
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Young SL, Jain N, Tatham AJ. The application of advanced imaging techniques in glaucoma. EXPERT REVIEW OF OPHTHALMOLOGY 2022. [DOI: 10.1080/17469899.2022.2101449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Su Ling Young
- Princess Alexandra Eye Pavilion, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Nikhil Jain
- Addenbrooke’s Hospital, Cambridge University Hospitals NHS trust, Cambridge, UK
| | - Andrew J Tatham
- Princess Alexandra Eye Pavilion, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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18
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Alexopoulos P, Madu C, Wollstein G, Schuman JS. The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques. Front Med (Lausanne) 2022; 9:891369. [PMID: 35847772 PMCID: PMC9279625 DOI: 10.3389/fmed.2022.891369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/22/2022] Open
Abstract
The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.
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Affiliation(s)
- Palaiologos Alexopoulos
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Chisom Madu
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
| | - Joel S. Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
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