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Fang J, Xing A, Chen Y, Zhou F. SeqCorr-EUNet: A sequence correction dual-flow network for segmentation and quantification of anterior segment OCT image. Comput Biol Med 2024; 171:108143. [PMID: 38364662 DOI: 10.1016/j.compbiomed.2024.108143] [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: 09/17/2023] [Revised: 01/16/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
The accurate segmentation of AS-OCT images is a prerequisite for the morphological details analysis of anterior segment structure and the extraction of clinical biological parameters, which play an essential role in the diagnosis, evaluation, and preoperative prognosis management of many ophthalmic diseases. Manually marking the boundaries of the anterior segment tissue is time-consuming and error-prone, with inherent speckle noise, various artifacts, and some low-quality scanned images further increasing the difficulty of the segmentation task. In this work, we propose a novel model called SeqCorr-EUNet with a dual-flow architecture based on convolutional gated recursive sequence correction for semantic segmentation and quantification of AS-OCT images. An EfficientNet encoder is employed to enhance the intra-slice features extraction ability of semantic segmentation flow. The sequence correction flow based on ConvGRU is introduced to extract inter-slice features from consecutive adjacent slices. Spatio-temporal information is fused to correct the morphological details of pre-segmentation results. And the channel attention gate is inserted into the skip-connection between encoder and decoder to enrich the contextual information and suppress the noise of irrelevant regions. Based on the segmentation results of the anterior segment structures, we achieved automatic extraction of essential clinical parameters, 3D reconstruction of the anterior chamber structure, and measurement of anterior chamber volume. The proposed SeqCorr-EUNet has been evaluated on the public AS-OCT dataset. The experimental results show that our method is competitive compared with the existing methods and significantly improves the segmentation and quantification performance of low-quality imaging structures in AS-OCT images.
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Affiliation(s)
- Jing Fang
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, Anhui, China.
| | - Aoyu Xing
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, Anhui, China.
| | - Ying Chen
- Department of Ophthalmology, Hospital of University of Science and Technology of China, Hefei, 230026, Anhui, China.
| | - Fang Zhou
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, Anhui, China.
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2
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Soh ZD, Tan M, Nongpiur ME, Xu BY, Friedman D, Zhang X, Leung C, Liu Y, Koh V, Aung T, Cheng CY. Assessment of angle closure disease in the age of artificial intelligence: A review. Prog Retin Eye Res 2024; 98:101227. [PMID: 37926242 DOI: 10.1016/j.preteyeres.2023.101227] [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: 05/31/2023] [Revised: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 11/07/2023]
Abstract
Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disease assessment and monitoring. Artificial intelligence (AI)- assisted tools to detect and assess PACD have proliferated in recent years with encouraging results. Machine learning (ML) algorithms that utilize clinical data have been developed to categorize angle closure eyes by disease mechanism. Other ML algorithms that utilize image data have demonstrated good performance in detecting angle closure. Nonetheless, deep learning (DL) algorithms trained directly on image data generally outperformed traditional ML algorithms in detecting PACD, were able to accurately differentiate between angle status (open, narrow, closed), and automated the measurement of quantitative parameters. However, more work is required to expand the capabilities of these AI algorithms and for deployment into real-world practice settings. This includes the need for real-world evaluation, establishing the use case for different algorithms, and evaluating the feasibility of deployment while considering other clinical, economic, social, and policy-related factors.
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Affiliation(s)
- Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore.
| | - Mingrui Tan
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*Star), 1 Fusionopolis Way, 138632, Singapore.
| | - Monisha Esther Nongpiur
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore.
| | - Benjamin Yixing Xu
- Roski Eye Institute, Keck School of Medicine, University of Southern California, 1450 San Pablo St #4400, Los Angeles, CA, 90033, USA.
| | - David Friedman
- Department of Ophthalmology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA; Massachusetts Eye and Ear, Mass General Brigham, 243 Charles Street, Boston, MA, 02114, USA.
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat Sen University, No. 54 Xianlie South Road, Yuexiu District, Guangzhou, China.
| | - Christopher Leung
- Department of Ophthalmology, School of Clinical Medicine, The University of Hong Kong, Cyberport 4, 100 Cyberport Road, Hong Kong; Department of Ophthalmology, Queen Mary Hospital, 102 Pok Fu Lam Road, Hong Kong.
| | - Yong Liu
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*Star), 1 Fusionopolis Way, 138632, Singapore.
| | - Victor Koh
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore; Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 7, 119228, Singapore.
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore.
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore; Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 7, 119228, Singapore.
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3
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Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition. Comput Biol Med 2022; 147:105752. [DOI: 10.1016/j.compbiomed.2022.105752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/22/2022]
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4
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Abstract
Early detection and monitoring are critical to the diagnosis and management of glaucoma, a progressive optic neuropathy that causes irreversible blindness. Optical coherence tomography (OCT) has become a commonly utilized imaging modality that aids in the detection and monitoring of structural glaucomatous damage. Since its inception in 1991, OCT has progressed through multiple iterations, from time-domain OCT, to spectral-domain OCT, to swept-source OCT, all of which have progressively improved the resolution and speed of scans. Even newer technological advancements and OCT applications, such as adaptive optics, visible-light OCT, and OCT-angiography, have enriched the use of OCT in the evaluation of glaucoma. This article reviews current commercial and state-of-the-art OCT technologies and analytic techniques in the context of their utility for glaucoma diagnosis and management, as well as promising future directions. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Alexi Geevarghese
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA;
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA; .,Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, New York 11201, USA.,Center for Neural Science, NYU College of Arts and Sciences, New York, NY 10003, USA
| | - Hiroshi Ishikawa
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA; .,Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, New York 11201, USA
| | - Joel S Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA; .,Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, New York 11201, USA.,Center for Neural Science, NYU College of Arts and Sciences, New York, NY 10003, USA.,Department of Physiology and Neuroscience, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA
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5
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Li W, Chen Q, Jiang C, Shi G, Deng G, Sun X. Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images. Transl Vis Sci Technol 2021; 10:19. [PMID: 34111263 PMCID: PMC8142723 DOI: 10.1167/tvst.10.6.19] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Purpose The purpose of this study was to develop a software package for the automatic classification of anterior chamber angle using anterior segment optical coherence tomography (AS-OCT). Methods AS-OCT images were collected from subjects with open, narrow, and closure anterior chamber angles, which were graded based on ultrasound biomicroscopy (UBM) results. The Inception version 3 network and the transfer learning technique were applied in the design of an algorithm for anterior chamber angle classification. The classification performance was evaluated by fivefold cross-validation and on an independent test dataset. Results The proposed algorithm reached a sensitivity of 0.999 and specificity of 1.000 in the judgment of closed and nonclosed angles. The overall classification of the proposed method in open angle, narrow angle, and angle-closure classifications reached a sensitivity of 0.989 and specificity of 0.995. Additionally, the sensitivity and specificity reached 1.000 and 1.000 for angle-closure, 0.983 and 0.993 for narrow angle, and 0.985 and 0.991 for open angle. Conclusions The experimental results showed that the proposed method can achieve a high accuracy of anterior chamber angle classification using AS-OCT images, and could be of value in future practice. Translational Relevance The proposed deep learning-based method that automate the classification of anterior chamber angle can facilitate clinical assessment of glaucoma.
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Affiliation(s)
- Wanyue Li
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Qian Chen
- Eye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.,Key NHC Key Laboratory of Myopia (Fudan University); Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Chunhui Jiang
- Eye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.,Key NHC Key Laboratory of Myopia (Fudan University); Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Guohua Shi
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Guohua Deng
- Department of Ophthalmology, the Third People's Hospital of Changzhou, Changzhou, Jiangsu, China
| | - Xinghuai Sun
- Eye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.,Key NHC Key Laboratory of Myopia (Fudan University); Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
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6
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Thomas A, Sunija AP, Manoj R, Ramachandran R, Ramachandran S, Varun PG, Palanisamy P. RPE layer detection and baseline estimation using statistical methods and randomization for classification of AMD from retinal OCT. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105822. [PMID: 33190943 DOI: 10.1016/j.cmpb.2020.105822] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 10/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Age-related macular degeneration (AMD) is a condition of the eye that affects the aged people. Optical coherence tomography (OCT) is a diagnostic tool capable of analyzing and identifying the disease affected retinal layers with high resolution. The objective of this work is to extract the retinal pigment epithelium (RPE) layer and the baseline (natural eye curvature, particular to every patient) from retinal spectral-domain OCT (SD-OCT) images. It uses them to find the height of drusen (abnormalities) in the RPE layer and classify it as AMD or normal. METHODS In the proposed work, the contrast enhancement based adaptive denoising technique is used for speckle elimination. Pixel grouping and iterative elimination based on the knowledge of typical layer intensities and positions are used to obtain the RPE layer. Using this estimate, randomization techniques are employed, followed by polynomial fitting and drusen removal to arrive at a baseline estimate. The classification is based on the drusen height obtained by taking the difference between the RPE and baseline levels. We have used a patient, wise classification approach where a patient is classified diseased if more than a threshold number of patient images have drusen of more than a certain height. Since all slices of an affected patient will not show drusen, we are justified in adopting this technique. RESULTS The proposed method is tested on a public data set of 2130 images/slices, which belonged to 30 patient volumes (15 AMD and 15 Normal) and achieved an overall accuracy of 96.66%, with no false positives. In comparison with existing works, the proposed method achieved higher overall accuracy and a better baseline estimate. CONCLUSIONS The proposed work focuses on AMD/normal classification using a statistical approach. It does not require any training. The proposed method modifies the motion restoration paradigm to obtain an application-specific denoising algorithm. The existing RPE detection algorithm is modified significantly to make it robust and applicable even to images where the RPE is not very evident/there is a significant amount of perforations (drusen). The baseline estimation algorithm employs a powerful combination of randomization, iterative polynomial fitting, and pixel elimination in contrast to mere fitting techniques. The main highlight of this work is, it achieved an exact estimation of the baseline in the retinal image compared to the existing methods.
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Affiliation(s)
- Anju Thomas
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - A P Sunija
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - Rigved Manoj
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - Rajiv Ramachandran
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - Srikkanth Ramachandran
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - P Gopi Varun
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
| | - P Palanisamy
- Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamilnadu 620015, India.
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7
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Automated classification of diabetic retinopathy through reliable feature selection. Phys Eng Sci Med 2020; 43:927-945. [DOI: 10.1007/s13246-020-00890-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 06/23/2020] [Indexed: 02/08/2023]
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8
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Fu H, Xu Y, Lin S, Wong DWK, Baskaran M, Mahesh M, Aung T, Liu J. Angle-Closure Detection in Anterior Segment OCT Based on Multilevel Deep Network. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:3358-3366. [PMID: 30794201 DOI: 10.1109/tcyb.2019.2897162] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Irreversible visual impairment is often caused by primary angle-closure glaucoma, which could be detected via anterior segment optical coherence tomography (AS-OCT). In this paper, an automated system based on deep learning is presented for angle-closure detection in AS-OCT images. Our system learns a discriminative representation from training data that captures subtle visual cues not modeled by handcrafted features. A multilevel deep network is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: 1) the global anterior segment structure; 2) local iris region; and 3) anterior chamber angle (ACA) patch. In our method, a sliding window-based detector is designed to localize the ACA region, which addresses ACA detection as a regression task. Then, three parallel subnetworks are applied to extract AS-OCT representations for the global image and at clinically relevant local regions. Finally, the extracted deep features of these subnetworks are concatenated into one fully connected layer to predict the angle-closure detection result. In the experiments, our system is shown to surpass previous detection methods and other deep learning systems on two clinical AS-OCT datasets.
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9
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Ting DSJ, Foo VH, Yang LWY, Sia JT, Ang M, Lin H, Chodosh J, Mehta JS, Ting DSW. Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology. Br J Ophthalmol 2020; 105:158-168. [PMID: 32532762 DOI: 10.1136/bjophthalmol-2019-315651] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 02/21/2020] [Accepted: 03/24/2020] [Indexed: 12/12/2022]
Abstract
With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for 'intelligent' healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.
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Affiliation(s)
- Darren Shu Jeng Ting
- Academic Ophthalmology, University of Nottingham, Nottingham, UK.,Department of Ophthalmology, Queen's Medical Centre, Nottingham, UK.,Singapore Eye Research Institute, Singapore
| | | | | | - Josh Tjunrong Sia
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore.,Cornea And Ext Disease, Singapore National Eye Centre, Singapore
| | - Haotian Lin
- Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, China
| | - James Chodosh
- Ophthalmology, Massachusetts Eye and Ear Infirmary Howe Laboratory Harvard Medical School, Boston, Massachusetts, USA
| | - Jodhbir S Mehta
- Singapore Eye Research Institute, Singapore.,Cornea And Ext Disease, Singapore National Eye Centre, Singapore
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore .,Vitreo-retinal Department, Singapore National Eye Center, Singapore
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10
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Wu X, Liu L, Zhao L, Guo C, Li R, Wang T, Yang X, Xie P, Liu Y, Lin H. Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:714. [PMID: 32617334 PMCID: PMC7327317 DOI: 10.21037/atm-20-976] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Artificial intelligence (AI) based on machine learning (ML) and deep learning (DL) techniques has gained tremendous global interest in this era. Recent studies have demonstrated the potential of AI systems to provide improved capability in various tasks, especially in image recognition field. As an image-centric subspecialty, ophthalmology has become one of the frontiers of AI research. Trained on optical coherence tomography, slit-lamp images and even ordinary eye images, AI can achieve robust performance in the detection of glaucoma, corneal arcus and cataracts. Moreover, AI models based on other forms of data also performed satisfactorily. Nevertheless, several challenges with AI application in ophthalmology have also arisen, including standardization of data sets, validation and applicability of AI models, and ethical issues. In this review, we provided a summary of the state-of-the-art AI application in anterior segment ophthalmic diseases, potential challenges in clinical implementation and our prospects.
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Affiliation(s)
- Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Lixue Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Chong Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Ruiyang Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Ting Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaonan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Peichen Xie
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.,Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
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11
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[Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography]. Ophthalmologe 2019; 115:714-721. [PMID: 29675699 DOI: 10.1007/s00347-018-0706-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Deep learning is increasingly becoming the focus of various imaging methods in medicine. Due to the large number of different imaging modalities, ophthalmology is particularly suitable for this field of application. This article gives a general overview on the topic of deep learning and its current applications in the field of optical coherence tomography. For the benefit of the reader it focuses on the clinical rather than the technical aspects.
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12
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Unsupervised feature extraction of anterior chamber OCT images for ordering and classification. Sci Rep 2019; 9:1157. [PMID: 30718688 PMCID: PMC6362085 DOI: 10.1038/s41598-018-38136-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 12/19/2018] [Indexed: 11/24/2022] Open
Abstract
We propose an image processing method for ordering anterior chamber optical coherence tomography (OCT) images in a fully unsupervised manner. The method consists of three steps: Firstly we preprocess the images (filtering the noise, aligning and normalizing the resolution); secondly, a distance measure between images is computed for every pair of images; thirdly we apply a machine learning algorithm that exploits the distance measure to order the images in a two-dimensional plane. The method is applied to a large (~1000) database of anterior chamber OCT images of healthy subjects and patients with angle-closure and the resulting unsupervised ordering and classification is validated by two ophthalmologists.
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13
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Alizadehsani R, Hosseini MJ, Khosravi A, Khozeimeh F, Roshanzamir M, Sarrafzadegan N, Nahavandi S. Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:119-127. [PMID: 29903478 DOI: 10.1016/j.cmpb.2018.05.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 04/24/2018] [Accepted: 05/03/2018] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms. METHODS The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis. RESULTS This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before. CONCLUSION This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia
| | - Mohammad Javad Hosseini
- Department of Computer Science and Engineering, University of Washington, Seattle, United States
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia.
| | - Fahime Khozeimeh
- Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences,Isfahan,Iran & Faculty of Medicine, SPPH, University of British Columbia, Vancouver,BC, Canada
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia
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14
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Liu X, Fu T, Pan Z, Liu D, Hu W, Liu J, Zhang K. Automated Layer Segmentation of Retinal Optical Coherence Tomography Images Using a Deep Feature Enhanced Structured Random Forests Classifier. IEEE J Biomed Health Inform 2018; 23:1404-1416. [PMID: 30010602 DOI: 10.1109/jbhi.2018.2856276] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Optical coherence tomography (OCT) is a high-resolution and noninvasive imaging modality that has become one of the most prevalent techniques for ophthalmic diagnosis. Retinal layer segmentation is very crucial for doctors to diagnose and study retinal diseases. However, manual segmentation is often a time-consuming and subjective process. In this work, we propose a new method for automatically segmenting retinal OCT images, which integrates deep features and hand-designed features to train a structured random forests classifier. The deep convolutional features are learned from deep residual network. With the trained classifier, we can get the contour probability graph of each layer; finally, the shortest path is employed to achieve the final layer segmentation. The experimental results show that our method achieves good results with the mean layer contour error of 1.215 pixels, whereas that of the state of the art was 1.464 pixels, and achieves an F1-score of 0.885, which is also better than 0.863 that is obtained by the state of the art method.
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15
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Novel Automated Approach to Predict the Outcome of Laser Peripheral Iridotomy for Primary Angle Closure Suspect Eyes Using Anterior Segment Optical Coherence Tomography. J Med Syst 2018; 42:107. [DOI: 10.1007/s10916-018-0960-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 04/16/2018] [Indexed: 12/17/2022]
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Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.02.003] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Fu H, Xu Y, Lin S, Zhang X, Wong DWK, Liu J, Frangi AF, Baskaran M, Aung T. Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1930-1938. [PMID: 28499992 DOI: 10.1109/tmi.2017.2703147] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus high-definition-OCT data sets demonstrate the effectiveness of our approach.
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Niwas SI, Lin W, Bai X, Kwoh CK, Jay Kuo CC, Sng CC, Aquino MC, Chew PTK. Automated anterior segment OCT image analysis for Angle Closure Glaucoma mechanisms classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:65-75. [PMID: 27208522 DOI: 10.1016/j.cmpb.2016.03.018] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Revised: 02/10/2016] [Accepted: 03/17/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Angle closure glaucoma (ACG) is an eye disease prevalent throughout the world. ACG is caused by four major mechanisms: exaggerated lens vault, pupil block, thick peripheral iris roll, and plateau iris. Identifying the specific mechanism in a given patient is important because each mechanism requires a specific medication and treatment regimen. Traditional methods of classifying these four mechanisms are based on clinically important parameters measured from anterior segment optical coherence tomography (AS-OCT) images, which rely on accurate segmentation of the AS-OCT image and identification of the scleral spur in the segmented AS-OCT images by clinicians. METHODS In this work, a fully automated method of classifying different ACG mechanisms based on AS-OCT images is proposed. Since the manual diagnosis mainly based on the morphology of each mechanism, in this study, a complete set of morphological features is extracted directly from raw AS-OCT images using compound image transforms, from which a small set of informative features with minimum redundancy are selected and fed into a Naïve Bayes Classifier (NBC). RESULTS We achieved an overall accuracy of 89.2% and 85.12% with a leave-one-out cross-validation and 10-fold cross-validation method, respectively. This study proposes a fully automated way for the classification of different ACG mechanisms, which is without intervention of doctors and less subjective when compared to the existing methods. CONCLUSIONS We directly extracted the compound image transformed features from the raw AS-OCT images without any segmentation and parameter measurement. Our method provides a completely automated and efficient way for the classification of different ACG mechanisms.
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Affiliation(s)
- Swamidoss Issac Niwas
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore, Singapore.
| | - Weisi Lin
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore, Singapore.
| | - Xiaolong Bai
- The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, People's Republic of China; School of Electrical and Electronics Engineering, Nanyang Technological University (NTU), 639798 Singapore, Singapore.
| | - Chee Keong Kwoh
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore, Singapore.
| | - C-C Jay Kuo
- Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089, USA.
| | - Chelvin C Sng
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore, Singapore.
| | - Maria Cecilia Aquino
- Eye Surgery Centre, National University Health System (NUHS), 119228 Singapore, Singapore.
| | - Paul T K Chew
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore, Singapore.
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Niwas SI, Jakhetiya V, Lin W, Kwoh CK, Sng CC, Aquino MC, Victor K, Chew PTK. Complex wavelet based quality assessment for AS-OCT images with application to Angle Closure Glaucoma diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:13-21. [PMID: 27208517 DOI: 10.1016/j.cmpb.2016.03.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 02/14/2016] [Accepted: 03/08/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Angle closure disease in the eye can be detected using time-domain Anterior Segment Optical Coherence Tomography (AS-OCT). The Anterior Chamber (AC) characteristics can be quantified from AS-OCT image, which is dependent on the image quality at the image acquisition stage. To date, to the best of our knowledge there are no objective or automated subjective measurements to assess the quality of AS-OCT images. METHODS To address AS-OCT image quality assessment issue, we define a method for objective assessment of AS-OCT images using complex wavelet based local binary pattern features. These features are pooled using the Naïve Bayes classifier to obtain the final quality parameter. To evaluate the proposed method, a subjective assessment has been performed by clinical AS-OCT experts, who graded the quality of AS-OCT images on a scale of good, fair, and poor. This was done based on the ability to identify the AC structures including the position of the scleral spur. RESULTS We compared the results of the proposed objective assessment with the subjective assessments. From this comparison, it is validated that the proposed objective assessment has the ability of differentiating the good and fair quality AS-OCT images for glaucoma diagnosis from the poor quality AS-OCT images. CONCLUSIONS This proposed algorithm is an automated approach to evaluate the AS-OCT images with the intention for collecting of high quality data for further medical diagnosis. Our proposed quality index has the ability of automatic objective and quantitative assessment of AS-OCT image quality and this quality index is similar to glaucoma specialist.
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Affiliation(s)
- Swamidoss Issac Niwas
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore.
| | - Vinit Jakhetiya
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore; Department of Electronics and Computer Engineering, Hong Kong University of Science and Technology (HKUST), Hong Kong.
| | - Weisi Lin
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore.
| | - Chee Keong Kwoh
- School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore.
| | - Chelvin C Sng
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore.
| | | | - Koh Victor
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore.
| | - Paul T K Chew
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore.
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Learning ECOC Code Matrix for Multiclass Classification with Application to Glaucoma Diagnosis. J Med Syst 2016; 40:78. [DOI: 10.1007/s10916-016-0436-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Accepted: 01/07/2016] [Indexed: 10/22/2022]
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