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Driban M, Yan A, Selvam A, Ong J, Vupparaboina KK, Chhablani J. Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review. Int J Retina Vitreous 2024; 10:36. [PMID: 38654344 PMCID: PMC11036694 DOI: 10.1186/s40942-024-00554-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction. MAIN BODY In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations. SHORT CONCLUSION As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.
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Affiliation(s)
- Matthew Driban
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Audrey Yan
- Department of Medicine, West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA
| | - Amrish Selvam
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, USA
| | | | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Mookiah MRK, Puch-Solis R, Nic Daeid N. Identification of bullets fired from air guns using machine and deep learning methods. Forensic Sci Int 2023; 349:111734. [PMID: 37267700 DOI: 10.1016/j.forsciint.2023.111734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 03/20/2023] [Accepted: 05/17/2023] [Indexed: 06/04/2023]
Abstract
Ballistics (the linkage of bullets and cartridge cases to weapons) is a common type of evidence encountered in criminal cases around the world. The interest lies in determining whether two bullets were fired using the same firearm. This paper proposes an automated method to classify bullets from surface topography and Land Engraved Area (LEA) images of the fired pellets using machine and deep learning methods. The curvature of the surface topography was removed using loess fit and features were extracted using Empirical Mode Decomposition (EMD) followed by various entropy measures. The informative features were identified using minimum Redundancy Maximum Relevance (mRMR), finally the classification was performed using Support Vector Machines (SVM), Decision Tree (DT) and Random Forest (RF) classifiers. The results revealed a good predictive performance. In addition, the deep learning model DenseNet121 was used to classify the LEA images. DenseNet121 provided a higher predictive performance than SVM, DT and RF classifiers. Moreover, the Grad-CAM technique was used to visualise the discriminative regions in the LEA images. These results suggest that the proposed deep learning method can be used to expedite the linkage of projectiles to firearms and assist in ballistic examinations. In this work, the bullets that were compared were air pellets fired from both air rifles and a high velocity air pistol. Air guns were used to collect the data because they were more accessible than other firearms and could be used as a proxy, delivering comparable LEAs. The methods developed here can be used as a proof-of-concept and are easily expandable to bullet and cartridge case identification from any weapon.
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Affiliation(s)
- Muthu Rama Krishnan Mookiah
- Leverhulme Research Centre for Forensic Science, School of Science and Engineering, University of Dundee, Nethergate Dundee DD1 4HN, Scotland, UK.
| | - Roberto Puch-Solis
- Leverhulme Research Centre for Forensic Science, School of Science and Engineering, University of Dundee, Nethergate Dundee DD1 4HN, Scotland, UK
| | - Niamh Nic Daeid
- Leverhulme Research Centre for Forensic Science, School of Science and Engineering, University of Dundee, Nethergate Dundee DD1 4HN, Scotland, UK
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Martins TGDS, Schor P, Mendes LGA, Fowler S, Silva R. Use of artificial intelligence in ophthalmology: a narrative review. SAO PAULO MED J 2022; 140:837-845. [PMID: 36043665 PMCID: PMC9671570 DOI: 10.1590/1516-3180.2021.0713.r1.22022022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 02/22/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) deals with development of algorithms that seek to perceive one's environment and perform actions that maximize one's chance of successfully reaching one's predetermined goals. OBJECTIVE To provide an overview of the basic principles of AI and its main studies in the fields of glaucoma, retinopathy of prematurity, age-related macular degeneration and diabetic retinopathy. From this perspective, the limitations and potential challenges that have accompanied the implementation and development of this new technology within ophthalmology are presented. DESIGN AND SETTING Narrative review developed by a research group at the Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil. METHODS We searched the literature on the main applications of AI within ophthalmology, using the keywords "artificial intelligence", "diabetic retinopathy", "macular degeneration age-related", "glaucoma" and "retinopathy of prematurity," covering the period from January 1, 2007, to May 3, 2021. We used the MEDLINE database (via PubMed) and the LILACS database (via Virtual Health Library) to identify relevant articles. RESULTS We retrieved 457 references, of which 47 were considered eligible for intensive review and critical analysis. CONCLUSION Use of technology, as embodied in AI algorithms, is a way of providing an increasingly accurate service and enhancing scientific research. This forms a source of complement and innovation in relation to the daily skills of ophthalmologists. Thus, AI adds technology to human expertise.
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Affiliation(s)
- Thiago Gonçalves dos Santos Martins
- MD, PhD. Researcher, Department of Ophthalmology, Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil; Research Fellow, Department of Ophthalmology, Ludwig Maximilians University (LMU), Munich, Germany; and Doctoral Student, University of Coimbra (UC), Coimbra, Portugal
| | - Paulo Schor
- PhD. Professor, Department of Ophthalmology, Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil
| | | | - Susan Fowler
- RN, PhD. Certified Neuroscience Registered Nurse (CNRN) and Research Fellow of American Heart Association, Department of Ophthalmology, Orlando Health, Orlando, United States; Researcher, Department of Ophthalmology, Walden University, Minneapolis (MN), United States; and Researcher, Department of Ophthalmology, Thomas Edison State University (TESU), Trenton (NJ), United States
| | - Rufino Silva
- MD, PhD. Fellow of the European Board of Ophthalmology and Professor, Coimbra Institute for Clinical and Biomedical Research (iCBR), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Fellow, Department of Ophthalmology, Centro Hospitalar e Universitário de Coimbra (CHUC), Coimbra, Portugal; and Researcher, Association for Innovation and Biomedical Research on Light and Image (AIBILI), Coimbra, Portugal
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Automated method for real-time AMD screening of fundus images dedicated for mobile devices. Med Biol Eng Comput 2022; 60:1449-1479. [DOI: 10.1007/s11517-022-02546-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 03/06/2022] [Indexed: 01/01/2023]
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Abstract
PURPOSE To solve the problem of automatic grading of macular edema in retinal images in a more stable and reliable way and reduce the workload of ophthalmologists, an automatic detection and grading method of diabetic macular edema (DME) based on deep neural network is proposed. METHODS The enhanced green channels of fundus images are input into YOLO network for training and testing. DME is graded according to the distance of macula and hard exudate (HE). We use multiscale feature fusion to form more comprehensive features on different grain images to improve the effect of HE detection. We adopt K-means++ algorithm to cluster anchor box size and use loss of the original network to guide the regression of HE bounding box and improve the regression accuracy of anchor boxes. We increase the diversity of samples for sample training by data augmentation, including cropping, flipping and rotating of fundus images so that each batch of training data can better represent the distribution of samples. RESULTS The detection accuracy of the proposed method can reach 96% on MESSIDOR dataset. The detection rates of HE with high, median and low probability are 100%, 79.12% and 60.40%. CONCLUSION The proposed method exhibits a very good detection stability on healthy and diseased fundus images.
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An Image Examination System for Retinal Optic Disc mining and Analysis with Social Group Optimization Algorithm. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022. [DOI: 10.4018/ijsir.300370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This work aims to develop a hybrid image examination system to extract and evaluate the Optic Disc (OD) from the Age-related Macular Degeneration (AMD) and Non-AMD class Digital Fundus Retinal Image (DFRI). This work implements an image pre-processing through Shannon’s Entropy and Social Group Optimization (SE+SGO) based thresholding and image post-processing with Level Set Segmentation (LSS). A relative study among the extracted OD and the ground-truth is then executed to compute the vital Picture Similarity Parameters (PSP). This study also presents a detailed pixel level data analysis practice on the extracted OD. Finally, the performance of the LSS is then validated against the existing segmentation techniques, such as Chan-Vese, Active-Contour and k-means clustering. The proposed work is executed on the iChallenge-AMD-2018 DFRI (400 images) and the results confirm that, proposed hybrid tool helps to achieve better values of Jaccard (86.82%), Dice (91.78%), Accuracy (98.94%), Precision (92.86%), Sensitivity (94.06%), and Specificity (99.46%).
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Noninvasive Machine Learning Screening Model for Dacryocystitis Based on Ocular Surface Indicators. J Craniofac Surg 2021; 33:e23-e28. [PMID: 34267140 DOI: 10.1097/scs.0000000000007863] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Dacryocystitis is an orbital disease that can be easily misdiagnosed. The most common diagnostic tools for dacryocystitis are computed tomography, lacrimal duct angiography, and lacrimal tract irrigation. Yet, those are invasive methods, which are not conducive to extensive screening. OBJECTIVE To explore the significance of ocular surface indicators and demographic data in the screening of dacryocystitis. MATERIALS AND METHODS Data were prospectively collected from 56 patients with dacryocystitis (56 eyes) and 56 healthy individuals. Collected indicators included demographic information (gender, age), ocular surface data of tear meniscus height, objective scatter index (OSI), and clinical diagnosis. The model features were screened out by machine learning to establish a dacryocystitis screening model. RESULTS Tear meniscus height, OSI_maximum Lyapunov exponent, basic OSI, median of OSI, mean of OSI, slope coefficient of OSI linear regression, coefficient of variation in OSI, interquartile range of OSI, and other 8 parameters were used as model parameters to establish a dacryocystitis screening model with an overall detection accuracy of 85.71%. CONCLUSIONS This new screening model that is based on ocular surface indicators provides a new option for noninvasive screening of dacryocystitis.
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LBP-based information assisted intelligent system for COVID-19 identification. Comput Biol Med 2021; 134:104453. [PMID: 33957343 PMCID: PMC8087862 DOI: 10.1016/j.compbiomed.2021.104453] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/19/2021] [Accepted: 04/24/2021] [Indexed: 01/08/2023]
Abstract
A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.
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Dong L, Yang Q, Zhang RH, Wei WB. Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis. EClinicalMedicine 2021; 35:100875. [PMID: 34027334 PMCID: PMC8129891 DOI: 10.1016/j.eclinm.2021.100875] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Age-related macular degeneration (AMD) is one of the leading causes of vision loss in the elderly population. The application of artificial intelligence (AI) provides convenience for the diagnosis of AMD. This systematic review and meta-analysis aimed to quantify the performance of AI in detecting AMD in fundus photographs. METHODS We searched PubMed, Embase, Web of Science and the Cochrane Library before December 31st, 2020 for studies reporting the application of AI in detecting AMD in color fundus photographs. Then, we pooled the data for analysis. PROSPERO registration number: CRD42020197532. FINDINGS 19 studies were finally selected for systematic review and 13 of them were included in the quantitative synthesis. All studies adopted human graders as reference standard. The pooled area under the receiver operating characteristic curve (AUROC) was 0.983 (95% confidence interval (CI):0.979-0.987). The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were 0.88 (95% CI:0.88-0.88), 0.90 (95% CI:0.90-0.91), and 275.27 (95% CI:158.43-478.27), respectively. Threshold analysis was performed and a potential threshold effect was detected among the studies (Spearman correlation coefficient: -0.600, P = 0.030), which was the main cause for the heterogeneity. For studies applying convolutional neural networks in the Age-Related Eye Disease Study database, the pooled AUROC, sensitivity, specificity, and DOR were 0.983 (95% CI:0.978-0.988), 0.88 (95% CI:0.88-0.88), 0.91 (95% CI:0.91-0.91), and 273.14 (95% CI:130.79-570.43), respectively. INTERPRETATION Our data indicated that AI was able to detect AMD in color fundus photographs. The application of AI-based automatic tools is beneficial for the diagnosis of AMD. FUNDING Capital Health Research and Development of Special (2020-1-2052).
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Ng SJ, Ong HS, Tong L. A practical framework for telemedicine in dry eye disease. Ocul Surf 2020; 23:143-145. [PMID: 33127597 DOI: 10.1016/j.jtos.2020.10.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/13/2020] [Accepted: 10/25/2020] [Indexed: 11/25/2022]
Affiliation(s)
- Shi-Jie Ng
- School of Medicine, Trinity College Dublin, Ireland
| | - Hon Shing Ong
- Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore; Duke-NUS Medical School, Singapore
| | - Louis Tong
- Singapore Eye Research Institute, Singapore; Singapore National Eye Centre, Singapore; Duke-NUS Medical School, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Epiretinal Membrane Detection at the Ophthalmologist Level using Deep Learning of Optical Coherence Tomography. Sci Rep 2020; 10:8424. [PMID: 32439844 PMCID: PMC7242423 DOI: 10.1038/s41598-020-65405-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/04/2020] [Indexed: 12/23/2022] Open
Abstract
Purpose: Previous deep learning studies on optical coherence tomography (OCT) mainly focused on diabetic retinopathy and age-related macular degeneration. We proposed a deep learning model that can identify epiretinal membrane (ERM) in OCT with ophthalmologist-level performance. Design: Cross-sectional study. Participants: A total of 3,618 central fovea cross section OCT images from 1,475 eyes of 964 patients. Methods: We retrospectively collected 7,652 OCT images from 1,197 patients. From these images, 2,171 were normal and 1,447 were ERM OCT. A total of 3,141 OCT images was used as training dataset and 477 images as testing dataset. DL algorithm was used to train the interpretation model. Diagnostic results by four board-certified non-retinal specialized ophthalmologists on the testing dataset were compared with those generated by the DL model. Main Outcome Measures: We calculated for the derived DL model the following characteristics: sensitivity, specificity, F1 score and area under curve (AUC) of the receiver operating characteristic (ROC) curve. These were calculated according to the gold standard results which were parallel diagnoses of the retinal specialist. Performance of the DL model was finally compared with that of non-retinal specialized ophthalmologists. Results: Regarding the diagnosis of ERM in OCT images, the trained DL model had the following characteristics in performance: sensitivity: 98.7%, specificity: 98.0%, and F1 score: 0.945. The accuracy on the training dataset was 99.7% (95% CI: 99.4 - 99.9%), and for the testing dataset, diagnostic accuracy was 98.1% (95% CI: 96.5 - 99.1%). AUC of the ROC curve was 0.999. The DL model slightly outperformed the average non-retinal specialized ophthalmologists. Conclusions: An ophthalmologist-level DL model was built here to accurately identify ERM in OCT images. The performance of the model was slightly better than the average non-retinal specialized ophthalmologists. The derived model may play a role to assist clinicians to promote the efficiency and safety of healthcare in the future.
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Abstract
Artificial intelligence is advancing rapidly and making its way into all areas of our lives. This review discusses developments and potential practices regarding the use of artificial intelligence in the field of ophthalmology, and the related topic of medical ethics. Various artificial intelligence applications related to the diagnosis of eye diseases were researched in books, journals, search engines, print and social media. Resources were cross-checked to verify the information. Artificial intelligence algorithms, some of which were approved by the US Food and Drug Administration, have been adopted in the field of ophthalmology, especially in diagnostic studies. Studies are being conducted that prove that artificial intelligence algorithms can be used in the field of ophthalmology, especially in diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. Some of these algorithms have come to the approval stage. The current point in artificial intelligence studies shows that this technology has advanced considerably and shows promise for future work. It is believed that artificial intelligence applications will be effective in identifying patients with preventable vision loss and directing them to physicians, especially in developing countries where there are fewer trained professionals and physicians are difficult to reach. When we consider the possibility that some future artificial intelligence systems may be candidates for moral/ethical status, certain ethical issues arise. Questions about moral/ethical status are important in some areas of applied ethics. Although it is accepted that current intelligence systems do not have moral/ethical status, it has yet to be determined what the exact the characteristics that confer moral/ethical status are or will be.
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Affiliation(s)
- Kadircan Keskinbora
- Bahçeşehir University Faculty of Medicine, Department of Ophthalmology, Division of Medical Ethics and History of Medicine, İstanbul, Turkey
| | - Fatih Güven
- Health Sciences University Bakırköy Training and Research Hospital, Clinic of Ophthalmology, İstanbul, Turkey
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Keel S, Li Z, Scheetz J, Robman L, Phung J, Makeyeva G, Aung K, Liu C, Yan X, Meng W, Guymer R, Chang R, He M. Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs. Clin Exp Ophthalmol 2019; 47:1009-1018. [PMID: 31215760 DOI: 10.1111/ceo.13575] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 05/24/2019] [Accepted: 06/13/2019] [Indexed: 12/17/2022]
Abstract
IMPORTANCE Detection of early onset neovascular age-related macular degeneration (AMD) is critical to protecting vision. BACKGROUND To describe the development and validation of a deep-learning algorithm (DLA) for the detection of neovascular age-related macular degeneration. DESIGN Development and validation of a DLA using retrospective datasets. PARTICIPANTS We developed and trained the DLA using 56 113 retinal images and an additional 86 162 images from an independent dataset to externally validate the DLA. All images were non-stereoscopic and retrospectively collected. METHODS The internal validation dataset was derived from real-world clinical settings in China. Gold standard grading was assigned when consensus was reached by three individual ophthalmologists. The DLA classified 31 247 images as gradable and 24 866 as ungradable (poor quality or poor field definition). These ungradable images were used to create a classification model for image quality. Efficiency and diagnostic accuracy were tested using 86 162 images derived from the Melbourne Collaborative Cohort Study. Neovascular AMD and/or ungradable outcome in one or both eyes was considered referable. MAIN OUTCOME MEASURES Area under the receiver operating characteristic curve (AUC), sensitivity and specificity. RESULTS In the internal validation dataset, the AUC, sensitivity and specificity of the DLA for neovascular AMD was 0.995, 96.7%, 96.4%, respectively. Testing against the independent external dataset achieved an AUC, sensitivity and specificity of 0.967, 100% and 93.4%, respectively. More than 60% of false positive cases displayed other macular pathologies. Amongst the false negative cases (internal validation dataset only), over half (57.2%) proved to be undetected detachment of the neurosensory retina or RPE layer. CONCLUSIONS AND RELEVANCE This DLA shows robust performance for the detection of neovascular AMD amongst retinal images from a multi-ethnic sample and under different imaging protocols. Further research is warranted to investigate where this technology could be best utilized within screening and research settings.
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Affiliation(s)
- Stuart Keel
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Zhixi Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Jane Scheetz
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Liubov Robman
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Monash University Melbourne, Melbourne, Victoria, Australia
| | - James Phung
- Monash University Melbourne, Melbourne, Victoria, Australia
| | - Galina Makeyeva
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - KhinZaw Aung
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Chi Liu
- Healgoo Interactive Medical Technology Co. Ltd., Guangzhou, China
| | - Xixi Yan
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Wei Meng
- Healgoo Interactive Medical Technology Co. Ltd., Guangzhou, China
| | - Robyn Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert Chang
- Department of Ophthalmology, Byers Eye Institute at Stanford University, Palo Alto, California
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia.,State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
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Pead E, Megaw R, Cameron J, Fleming A, Dhillon B, Trucco E, MacGillivray T. Automated detection of age-related macular degeneration in color fundus photography: a systematic review. Surv Ophthalmol 2019; 64:498-511. [PMID: 30772363 PMCID: PMC6598673 DOI: 10.1016/j.survophthal.2019.02.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 01/31/2019] [Accepted: 02/04/2019] [Indexed: 12/13/2022]
Abstract
The rising prevalence of age-related eye diseases, particularly age-related macular degeneration, places an ever-increasing burden on health care providers. As new treatments emerge, it is necessary to develop methods for reliably assessing patients' disease status and stratifying risk of progression. The presence of drusen in the retina represents a key early feature in which size, number, and morphology are thought to correlate significantly with the risk of progression to sight-threatening age-related macular degeneration. Manual labeling of drusen on color fundus photographs by a human is labor intensive and is where automatic computerized detection would appreciably aid patient care. We review and evaluate current artificial intelligence methods and developments for the automated detection of drusen in the context of age-related macular degeneration.
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Affiliation(s)
- Emma Pead
- VAMPIRE Project, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland.
| | - Roly Megaw
- Princess Alexandra Eye Pavilion, Edinburgh, Scotland
| | - James Cameron
- MRC Human Genetics Unit, The University of Edinburgh, Edinburgh, Scotland
| | - Alan Fleming
- Optos plc, Queensferry House, Carnegie Campus, Dunfermline
| | | | - Emanuele Trucco
- VAMPIRE Project, Computing (School of Science and Engineering), University of Dundee, UK
| | - Thomas MacGillivray
- VAMPIRE Project, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
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Wang Y, Soetikno B, Furst J, Raicu D, Fawzi AA. Drusen diagnosis comparison between hyper-spectral and color retinal images. BIOMEDICAL OPTICS EXPRESS 2019; 10:914-931. [PMID: 30800523 PMCID: PMC6377880 DOI: 10.1364/boe.10.000914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/04/2019] [Accepted: 01/07/2019] [Indexed: 06/09/2023]
Abstract
Age-related macular degeneration (AMD) is a degenerative aging disorder, which can lead to irreversible vision loss in older individuals. The emergence of clinical applications of retinal hyper-spectral imaging provides a unique opportunity to capture important spectral signatures, with the potential to enhance the molecular diagnosis of retinal diseases. In this study, we use a machine learning classification approach to explore whether hyper-spectral images offer an improved outcome compared to standard RGB images. Our results show that the classifier performs better on hyper-spectral images with improved accuracy and sensitivity for drusen classification compared to standard imaging. By examining the most important features in the classification task, our data suggest that drusen are highly heterogeneous. Our work provides further evidence that hyper-spectral retinal image data are uniquely suited for computer-aided diagnosis and detection techniques.
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Affiliation(s)
- Yiyang Wang
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Brian Soetikno
- Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Functional Optical Imaging Laboratory, Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Jacob Furst
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Daniela Raicu
- College of Computing and Digital Media, DePaul University, Chicago, Illinois, 60604, USA
| | - Amani A Fawzi
- Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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Li Z, Keel S, Liu C, He M. Can Artificial Intelligence Make Screening Faster, More Accurate, and More Accessible? Asia Pac J Ophthalmol (Phila) 2018; 7:436-441. [PMID: 30556381 DOI: 10.22608/apo.2018438] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Diabetic retinopathy, glaucoma, and age-related macular degeneration are leading causes of vision loss and blindness worldwide. They tend to be asymptomatic in the early phase of disease and therefore require active screening programs to identify the patients requiring referral and treatment. Deep learning-based artificial intelligence technology has recently become a major topic in the field of ophthalmology. This paper aimed to provide a general view of the major findings on the application of deep learning for the classification of eye diseases from common imaging modalities. In the future, it is expected that these technologies will be applied in real-world screening programs to improve their efficiency and affordability.
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Affiliation(s)
- Zhixi Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Stuart Keel
- Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Chi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Centre for Eye Research Australia, Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
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17
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Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017. INFORMATICS IN MEDICINE UNLOCKED 2018. [DOI: 10.1016/j.imu.2018.03.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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18
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Koh JEW, Ng EYK, Bhandary SV, Laude A, Acharya UR. Automated detection of retinal health using PHOG and SURF features extracted from fundus images. APPL INTELL 2017. [DOI: 10.1007/s10489-017-1048-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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19
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Acharya UR, Mookiah MRK, Koh JE, Tan JH, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A. Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index. Comput Biol Med 2017; 84:59-68. [DOI: 10.1016/j.compbiomed.2017.03.016] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 03/16/2017] [Accepted: 03/17/2017] [Indexed: 12/11/2022]
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20
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Koh JE, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Sree SV, Bhandary SV, Rao AK, Sivaprasad S, Chua KC, Laude A, Tong L. Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies. Comput Biol Med 2017; 84:89-97. [DOI: 10.1016/j.compbiomed.2017.03.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 02/16/2017] [Accepted: 03/12/2017] [Indexed: 10/20/2022]
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21
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Wang L, Zhang K, Liu X, Long E, Jiang J, An Y, Zhang J, Liu Z, Lin Z, Li X, Chen J, Cao Q, Li J, Wu X, Wang D, Li W, Lin H. Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images. Sci Rep 2017; 7:41545. [PMID: 28139688 PMCID: PMC5282520 DOI: 10.1038/srep41545] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 12/22/2016] [Indexed: 11/16/2022] Open
Abstract
There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.
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Affiliation(s)
- Liming Wang
- Institute of Software Engineering, Xidian University, Xi'an 710071, China
| | - Kai Zhang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Xiyang Liu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China.,School of Software, Xidian University, Xi'an 710071, China
| | - Erping Long
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Jiewei Jiang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Yingying An
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Jia Zhang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, China
| | - Zhenzhen Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Zhuoling Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Xiaoyan Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Jingjing Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Qianzhong Cao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Jing Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Dongni Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Wangting Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
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22
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Wang Y, Zhang Y, Yao Z, Zhao R, Zhou F. Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. BIOMEDICAL OPTICS EXPRESS 2016; 7:4928-4940. [PMID: 28018716 PMCID: PMC5175542 DOI: 10.1364/boe.7.004928] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 10/05/2016] [Accepted: 10/05/2016] [Indexed: 05/05/2023]
Abstract
Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples.
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Affiliation(s)
- Yu Wang
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning 110169, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Yaonan Zhang
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning 110169, China; College of Electronics and Information Engineering, Xi'an Siyuan University, Xi'an 710038, China;
| | - Zhaomin Yao
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning 110169, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Ruixue Zhao
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China; ; ; http://www.healthinformaticslab.org/ffzhou/
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23
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Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Rao AK, Fujita H, Hagiwara Y, Chua CK, Laude A. Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. Comput Biol Med 2016; 75:54-62. [PMID: 27253617 DOI: 10.1016/j.compbiomed.2016.04.015] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 04/07/2016] [Accepted: 04/22/2016] [Indexed: 11/27/2022]
Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia
| | | | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India
| | - A Krishna Rao
- Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India
| | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, 020-0693, Japan
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Chua Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore
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24
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Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Noronha K, Bhandary SV, Rao AK, Hagiwara Y, Chua CK, Laude A. Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. Comput Biol Med 2016; 73:131-40. [PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 04/10/2016] [Accepted: 04/14/2016] [Indexed: 11/18/2022]
Abstract
Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia
| | | | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Jen Hong Tan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Kevin Noronha
- Department of Electronics and Telecommunication, St. Francis Institute of Technology, Mumbai 400103, India
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - A Krishna Rao
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - Yuki Hagiwara
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Chua Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, 308433, Singapore
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25
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Huang L, Zhang H, Cheng CY, Wen F, Tam POS, Zhao P, Chen H, Li Z, Chen L, Tai Z, Yamashiro K, Deng S, Zhu X, Chen W, Cai L, Lu F, Li Y, Cheung CMG, Shi Y, Miyake M, Lin Y, Gong B, Liu X, Sim KS, Yang J, Mori K, Zhang X, Cackett PD, Tsujikawa M, Nishida K, Hao F, Ma S, Lin H, Cheng J, Fei P, Lai TYY, Tang S, Laude A, Inoue S, Yeo IY, Sakurada Y, Zhou Y, Iijima H, Honda S, Lei C, Zhang L, Zheng H, Jiang D, Zhu X, Wong TY, Khor CC, Pang CP, Yoshimura N, Yang Z. A missense variant in FGD6 confers increased risk of polypoidal choroidal vasculopathy. Nat Genet 2016; 48:640-7. [PMID: 27089177 DOI: 10.1038/ng.3546] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 03/16/2016] [Indexed: 12/17/2022]
Abstract
Polypoidal choroidal vasculopathy (PCV), a subtype of 'wet' age-related macular degeneration (AMD), constitutes up to 55% of cases of wet AMD in Asian patients. In contrast to the choroidal neovascularization (CNV) subtype, the genetic risk factors for PCV are relatively unknown. Exome sequencing analysis of a Han Chinese cohort followed by replication in four independent cohorts identified a rare c.986A>G (p.Lys329Arg) variant in the FGD6 gene as significantly associated with PCV (P = 2.19 × 10(-16), odds ratio (OR) = 2.12) but not with CNV (P = 0.26, OR = 1.13). The intracellular localization of FGD6-Arg329 is distinct from that of FGD6-Lys329. In vitro, FGD6 could regulate proangiogenic activity, and oxidized phospholipids increased expression of FGD6. FGD6-Arg329 promoted more abnormal vessel development in the mouse retina than FGD6-Lys329. Collectively, our data suggest that oxidized phospholipids and FGD6-Arg329 might act synergistically to increase susceptibility to PCV.
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Affiliation(s)
- Lulin Huang
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Institute of Chengdu Biology, Chinese Academy of Sciences, Chengdu, China.,Sichuan Translational Medicine Hospital, Chinese Academy of Sciences, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Houbin Zhang
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Duke-National University of Singapore Graduate Medical School, Singapore
| | - Feng Wen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Pancy O S Tam
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Peiquan Zhao
- Department of Ophthalmology, Xinhua Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Haoyu Chen
- Joint Shantou International Eye Center, Shantou University and Chinese University of Hong Kong, Shantou, China
| | - Zheng Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Human Genetics, Genome Institute of Singapore, Singapore
| | - Lijia Chen
- Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong, China
| | - Zhengfu Tai
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Institute of Chengdu Biology, Chinese Academy of Sciences, Chengdu, China.,Sichuan Translational Medicine Hospital, Chinese Academy of Sciences, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Kenji Yamashiro
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shaoping Deng
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xianjun Zhu
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Weiqi Chen
- Joint Shantou International Eye Center, Shantou University and Chinese University of Hong Kong, Shantou, China
| | - Li Cai
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Fang Lu
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanfeng Li
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Chui-Ming G Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yi Shi
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Masahiro Miyake
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yin Lin
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Gong
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoqi Liu
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Kar-Seng Sim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Department of Human Genetics, Genome Institute of Singapore, Singapore
| | - Jiyun Yang
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Keisuke Mori
- Department of Ophthalmology, Saitama Medical University, Iruma, Japan
| | - Xiongzhe Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Peter D Cackett
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Princess Alexandra Eye Pavilion, Edinburgh, UK
| | - Motokazu Tsujikawa
- Department of Ophthalmology, Osaka University Medical School, Osaka, Japan
| | - Kohji Nishida
- Department of Ophthalmology, Osaka University Medical School, Osaka, Japan
| | - Fang Hao
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shi Ma
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - He Lin
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Cheng
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ping Fei
- Department of Ophthalmology, Xinhua Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Timothy Y Y Lai
- Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong, China
| | - Sibo Tang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Augustinus Laude
- National Health care Group Eye Institute, Tan Tock Seng Hospital, Singapore
| | - Satoshi Inoue
- Division of Gene Regulation and Signal Transduction, Research Center for Genomic Medicine, Saitama Medical University, Saitama, Japan
| | - Ian Y Yeo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Duke-National University of Singapore Graduate Medical School, Singapore
| | - Yoichi Sakurada
- Department of Surgery, Division of Ophthalmology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yu Zhou
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hiroyuki Iijima
- Department of Ophthalmology, Faculty of Medicine, University of Yamanashi, Yamanashi, Japan
| | - Shigeru Honda
- Department of Surgery, Division of Ophthalmology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Chuntao Lei
- Department of Ophthalmology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - Lin Zhang
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Zheng
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Dan Jiang
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiong Zhu
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Tien-Ying Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Duke-National University of Singapore Graduate Medical School, Singapore
| | - Chiea-Chuen Khor
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Human Genetics, Genome Institute of Singapore, Singapore
| | - Chi-Pui Pang
- Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong, China
| | - Nagahisa Yoshimura
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Zhenglin Yang
- Key Laboratory for Human Disease Gene Study, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Institute of Chengdu Biology, Chinese Academy of Sciences, Chengdu, China.,Sichuan Translational Medicine Hospital, Chinese Academy of Sciences, Chengdu, China.,Center of Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, China
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26
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Mookiah MRK, Acharya UR, Fujita H, Koh JE, Tan JH, Chua CK, Bhandary SV, Noronha K, Laude A, Tong L. Automated detection of age-related macular degeneration using empirical mode decomposition. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.09.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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27
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Feeny AK, Tadarati M, Freund DE, Bressler NM, Burlina P. Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images. Comput Biol Med 2015; 65:124-36. [PMID: 26318113 DOI: 10.1016/j.compbiomed.2015.06.018] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 06/19/2015] [Accepted: 06/20/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND Age-related macular degeneration (AMD), left untreated, is the leading cause of vision loss in people older than 55. Severe central vision loss occurs in the advanced stage of the disease, characterized by either the in growth of choroidal neovascularization (CNV), termed the "wet" form, or by geographic atrophy (GA) of the retinal pigment epithelium (RPE) involving the center of the macula, termed the "dry" form. Tracking the change in GA area over time is important since it allows for the characterization of the effectiveness of GA treatments. Tracking GA evolution can be achieved by physicians performing manual delineation of GA area on retinal fundus images. However, manual GA delineation is time-consuming and subject to inter-and intra-observer variability. METHODS We have developed a fully automated GA segmentation algorithm in color fundus images that uses a supervised machine learning approach employing a random forest classifier. This algorithm is developed and tested using a dataset of images from the NIH-sponsored Age Related Eye Disease Study (AREDS). GA segmentation output was compared against a manual delineation by a retina specialist. RESULTS Using 143 color fundus images from 55 different patient eyes, our algorithm achieved PPV of 0.82±0.19, and NPV of 0:95±0.07. DISCUSSION This is the first study, to our knowledge, applying machine learning methods to GA segmentation on color fundus images and using AREDS imagery for testing. These preliminary results show promising evidence that machine learning methods may have utility in automated characterization of GA from color fundus images.
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Affiliation(s)
- Albert K Feeny
- Applied Physics Laboratory, The Johns Hopkins University, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, MD, USA
| | - Mongkol Tadarati
- Retina Division, Wilmer Eye Institute, The Johns Hopkins University, MD, USA; Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - David E Freund
- Applied Physics Laboratory, The Johns Hopkins University, MD, USA
| | - Neil M Bressler
- Retina Division, Wilmer Eye Institute, The Johns Hopkins University, MD, USA
| | - Philippe Burlina
- Applied Physics Laboratory, The Johns Hopkins University, MD, USA; Retina Division, Wilmer Eye Institute, The Johns Hopkins University, MD, USA; Department of Computer Science, The Johns Hopkins University, MD, USA
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Local configuration pattern features for age-related macular degeneration characterization and classification. Comput Biol Med 2015; 63:208-18. [PMID: 26093788 DOI: 10.1016/j.compbiomed.2015.05.019] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Revised: 05/25/2015] [Accepted: 05/26/2015] [Indexed: 12/30/2022]
Abstract
Age-related Macular Degeneration (AMD) is an irreversible and chronic medical condition characterized by drusen, Choroidal Neovascularization (CNV) and Geographic Atrophy (GA). AMD is one of the major causes of visual loss among elderly people. It is caused by the degeneration of cells in the macula which is responsible for central vision. AMD can be dry or wet type, however dry AMD is most common. It is classified into early, intermediate and late AMD. The early detection and treatment may help one to stop the progression of the disease. Automated AMD diagnosis may reduce the screening time of the clinicians. In this work, we have introduced LCP to characterize normal and AMD classes using fundus images. Linear Configuration Coefficients (CC) and Pattern Occurrence (PO) features are extracted from fundus images. These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz. Decision Tree (DT), Nearest Neighbour (k-NN), Naive Bayes (NB), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to classify normal and AMD classes. The performance of the system is evaluated using both private (Kasturba Medical Hospital, Manipal, India) and public domain datasets viz. Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) using ten-fold cross validation. The proposed approach yielded best performance with a highest average accuracy of 97.78%, sensitivity of 98.00% and specificity of 97.50% for STARE dataset using 22 significant features. Hence, this system can be used as an aiding tool to the clinicians during mass eye screening programs to diagnose AMD.
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Zhang JJ, Chu SJ, Sun XL, Zhang T, Shi WY. Bevacizumab modulates retinal pigment epithelial-to-mesenchymal transition via regulating Notch signaling. Int J Ophthalmol 2015; 8:245-9. [PMID: 25938035 DOI: 10.3980/j.issn.2222-3959.2015.02.06] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2014] [Accepted: 01/23/2015] [Indexed: 01/23/2023] Open
Abstract
AIM To investigate the effect of bevacizumab treatment on Notch signaling and the induction of epithelial-of-mesenchymal transition (EMT) in human retinal pigment epithelial cells (ARPE-19) in vitro. METHODS In vitro cultivated ARPE-19 cells were treated with 0.25 mg/mL bevacizumab for 12, 24, and 48h. Cell morphology changes were observed under an inverted microscope. The expression of zonula occludens-1 (ZO-1), vimentin and Notch-1 intracellular domain (NICD) was examined by immunofluorescence. The mRNA levels of ZO-1, α-SMA, Notch-1, Notch-2, Notch-4, Dll4, Jagged-1, RBP-Jk and Hes-1 expression were evaluated with quantitative real-time polymerase chain reaction (qRT-PCR). The protein levels of α-SMA, NICD, Hes-1 and Dll-4 expression were examined with Western blot. RESULTS Bevacizumab stimulation increased the expression of α-SMA and vimentin in ARPE-19 cells which changed into spindle-shaped fibroblast-like cells. Meanwhile, the mRNA expression of Hes-1 increased and the protein expression of Hes-1 and NICD also increased, which Notch signaling was activated. The mRNA expression of Notch-1, Jagged-1 and RBP-Jk increased at 48h, and while Dll4 mRNA and protein expression did not change after bevacizumab treatment. CONCLUSION Jagged-1/Notch-1 signaling may play a critical role in bevacizumab-induced EMT in ARPE-19 cells, which provides a novel insight into the pathogenesis of intravitreal bevacizumab-associated complication.
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Affiliation(s)
- Jing-Jing Zhang
- Qingdao University Medical College, Qingdao 266071, Shandong Province, China ; Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong Academy of Medical Sciences, Qingdao 266071, Shandong Province, China
| | - San-Jun Chu
- Qingdao University Medical College, Qingdao 266071, Shandong Province, China ; Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong Academy of Medical Sciences, Qingdao 266071, Shandong Province, China
| | - Xiao-Lei Sun
- Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong Academy of Medical Sciences, Qingdao 266071, Shandong Province, China
| | - Ting Zhang
- Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong Academy of Medical Sciences, Qingdao 266071, Shandong Province, China
| | - Wei-Yun Shi
- Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong Academy of Medical Sciences, Qingdao 266071, Shandong Province, China
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