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Nawaz M, Uvaliyev A, Bibi K, Wei H, Abaxi SMD, Masood A, Shi P, Ho HP, Yuan W. Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review. Comput Med Imaging Graph 2023; 108:102269. [PMID: 37487362 DOI: 10.1016/j.compmedimag.2023.102269] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/26/2023]
Abstract
Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images of the microanatomy of biological tissue in-vivo and at micrometer-scale resolution. OCT imaging has been widely used to diagnose and manage various medical diseases, such as macular degeneration, glaucoma, and coronary artery disease. Despite its wide range of applications, the segmentation of OCT images remains difficult due to the complexity of tissue structures and the presence of artifacts. In recent years, different approaches have been used for OCT image segmentation, such as intensity-based, region-based, and deep learning-based methods. This paper reviews the major advances in state-of-the-art OCT image segmentation techniques. It provides an overview of the advantages and limitations of each method and presents the most relevant research works related to OCT image segmentation. It also provides an overview of existing datasets and discusses potential clinical applications. Additionally, this review gives an in-depth analysis of machine learning and deep learning approaches for OCT image segmentation. It outlines challenges and opportunities for further research in this field.
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Affiliation(s)
- Mehmood Nawaz
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Adilet Uvaliyev
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Khadija Bibi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Hao Wei
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Sai Mu Dalike Abaxi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Anum Masood
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Peilun Shi
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Ho-Pui Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Wu Yuan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
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Ong CJT, Wong MYZ, Cheong KX, Zhao J, Teo KYC, Tan TE. Optical Coherence Tomography Angiography in Retinal Vascular Disorders. Diagnostics (Basel) 2023; 13:diagnostics13091620. [PMID: 37175011 PMCID: PMC10178415 DOI: 10.3390/diagnostics13091620] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 04/28/2023] [Accepted: 05/01/2023] [Indexed: 05/15/2023] Open
Abstract
Traditionally, abnormalities of the retinal vasculature and perfusion in retinal vascular disorders, such as diabetic retinopathy and retinal vascular occlusions, have been visualized with dye-based fluorescein angiography (FA). Optical coherence tomography angiography (OCTA) is a newer, alternative modality for imaging the retinal vasculature, which has some advantages over FA, such as its dye-free, non-invasive nature, and depth resolution. The depth resolution of OCTA allows for characterization of the retinal microvasculature in distinct anatomic layers, and commercial OCTA platforms also provide automated quantitative vascular and perfusion metrics. Quantitative and qualitative OCTA analysis in various retinal vascular disorders has facilitated the detection of pre-clinical vascular changes, greater understanding of known clinical signs, and the development of imaging biomarkers to prognosticate and guide treatment. With further technological improvements, such as a greater field of view and better image quality processing algorithms, it is likely that OCTA will play an integral role in the study and management of retinal vascular disorders. Artificial intelligence methods-in particular, deep learning-show promise in refining the insights to be gained from the use of OCTA in retinal vascular disorders. This review aims to summarize the current literature on this imaging modality in relation to common retinal vascular disorders.
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Affiliation(s)
- Charles Jit Teng Ong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Mark Yu Zheng Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Kai Xiong Cheong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Jinzhi Zhao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Kelvin Yi Chong Teo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (EYE ACP), Duke-NUS Medical School, Singapore 169857, Singapore
| | - Tien-En Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (EYE ACP), Duke-NUS Medical School, Singapore 169857, Singapore
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Wang CT, Chang YH, Tan GSW, Lee SY, Chan RVP, Wu WC, Tsai ASH. Optical Coherence Tomography and Optical Coherence Tomography Angiography in Pediatric Retinal Diseases. Diagnostics (Basel) 2023; 13:diagnostics13081461. [PMID: 37189561 DOI: 10.3390/diagnostics13081461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/10/2023] [Accepted: 04/16/2023] [Indexed: 05/17/2023] Open
Abstract
Indirect ophthalmoscopy and handheld retinal imaging are the most common and traditional modalities for the evaluation and documentation of the pediatric fundus, especially for pre-verbal children. Optical coherence tomography (OCT) allows for in vivo visualization that resembles histology, and optical coherence tomography angiography (OCTA) allows for non-invasive depth-resolved imaging of the retinal vasculature. Both OCT and OCTA were extensively used and studied in adults, but not in children. The advent of prototype handheld OCT and OCTA have allowed for detailed imaging in younger infants and even neonates in the neonatal care intensive unit with retinopathy of prematurity (ROP). In this review, we discuss the use of OCTA and OCTA in various pediatric retinal diseases, including ROP, familial exudative vitreoretinopathy (FEVR), Coats disease and other less common diseases. For example, handheld portable OCT was shown to detect subclinical macular edema and incomplete foveal development in ROP, as well as subretinal exudation and fibrosis in Coats disease. Some challenges in the pediatric age group include the lack of a normative database and the difficulty in image registration for longitudinal comparison. We believe that technological improvements in the use of OCT and OCTA will improve our understanding and care of pediatric retina patients in the future.
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Affiliation(s)
- Chung-Ting Wang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City 333, Taiwan
| | - Yin-Hsi Chang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City 333, Taiwan
| | - Gavin S W Tan
- Singapore National Eye Centre, Singapore, Singapore 168751, Singapore
- DUKE NUS Medical School, Singapore 169857, Singapore
| | - Shu Yen Lee
- Singapore National Eye Centre, Singapore, Singapore 168751, Singapore
- DUKE NUS Medical School, Singapore 169857, Singapore
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Illinois Eye and Ear Infirmary, Chicago, IL 60612, USA
| | - Wei-Chi Wu
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City 333, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City 333, Taiwan
| | - Andrew S H Tsai
- Singapore National Eye Centre, Singapore, Singapore 168751, Singapore
- DUKE NUS Medical School, Singapore 169857, Singapore
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Optical Coherence Tomography Angiography of the Intestine: How to Prevent Motion Artifacts in Open and Laparoscopic Surgery? Life (Basel) 2023; 13:life13030705. [PMID: 36983861 PMCID: PMC10055682 DOI: 10.3390/life13030705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/25/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
(1) Introduction. The problem that limits the intraoperative use of OCTA for the intestinal circulation diagnostics is the low informative value of OCTA images containing too many motion artifacts. The aim of this study is to evaluate the efficiency and safety of the developed unit for the prevention of the appearance of motion artifacts in the OCTA images of the intestine in both open and laparoscopic surgery in the experiment; (2) Methods. A high-speed spectral-domain multimodal optical coherence tomograph (IAP RAS, Russia) operating at a wavelength of 1310 nm with a spectral width of 100 μm and a power of 2 mW was used. The developed unit was tested in two groups of experimental animals—on minipigs (group I, n = 10, open abdomen) and on rabbits (group II, n = 10, laparoscopy). Acute mesenteric ischemia was modeled and then 1 h later the small intestine underwent OCTA evaluation. A total of 400 OCTA images of the intact and ischemic small intestine were obtained and analyzed. The quality of the obtained OCTA images was evaluated based on the score proposed in 2020 by the group of Magnin M. (3) Results. Without stabilization, OCTA images of the intestine tissues were informative only in 32–44% of cases in open surgery and in 14–22% of cases in laparoscopic surgery. A vacuum bowel stabilizer with a pressure deficit of 22–25 mm Hg significantly reduced the number of motion artifacts. As a result, the proportion of informative OCTA images in open surgery increased up to 86.5% (Χ2 = 200.2, p = 0.001), and in laparoscopy up to 60% (Χ2 = 148.3, p = 0.001). (4) Conclusions. The used vacuum tissue stabilizer enabled a significant increase in the proportion of informative OCTA images by significantly reducing the motion artifacts.
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Chen Y, Yuan Y, Zhang S, Yang S, Zhang J, Guo X, Huang W, Zhu Z, He M, Wang W. Retinal nerve fiber layer thinning as a novel fingerprint for cardiovascular events: results from the prospective cohorts in UK and China. BMC Med 2023; 21:24. [PMID: 36653845 PMCID: PMC9850527 DOI: 10.1186/s12916-023-02728-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 01/05/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Retinal structural abnormalities have been found to serve as biomarkers for cardiovascular disease (CVD). However, the association between retinal nerve fiber layer (RNFL) thickness and the incidence of CVD events remains inconclusive, and relevant longitudinal studies are lacking. Therefore, we aimed to examine this link in two prospective cohort studies. METHODS A total of 25,563 participants from UK Biobank who were initially free of CVD were included in the current study. Another 635 participants without retinopathy at baseline from the Chinese Guangzhou Diabetes Eye Study (GDES) were adopted as the validation set. Measurements of RNFL thickness in the macular (UK Biobank) and peripapillary (GDES) regions were obtained from optical coherence tomography (OCT). Adjusted hazard ratios (HRs), odd ratios (ORs), and 95% confidence intervals (CI) were calculated to quantify CVD risk. RESULTS Over a median follow-up period of 7.67 years, 1281 (5.01%) participants in UK Biobank developed CVD events. Each 5-μm decrease in macular RNFL thickness was associated with an 8% increase in incident CVD risk (HR = 1.08, 95% CI: 1.01-1.17, p = 0.033). Compared with participants in the highest tertile of RNFL thickness, the risk of incident CVD was significantly increased in participants in the lowest thickness tertile (HR = 1.18, 95% CI: 1.01-1.38, p = 0.036). In GDES, 29 (4.57%) patients developed CVD events within 3 years. Lower average peripapillary RNFL thickness was also associated with a higher CVD risk (OR = 1.35, 95% CI: 1.11-1.65, p = 0.003). The additive net reclassification improvement (NRI) was 21.8%, and the absolute NRI was 2.0% by addition of RNFL thickness over the Framingham risk score. Of 29 patients with incident CVD, 7 were correctly reclassified to a higher risk category while 1 was reclassified to a lower category, and 21 high risk patients were not reclassified. CONCLUSIONS RNFL thinning was independently associated with increased incident cardiovascular risk and improved reclassification capability, indicating RNFL thickness derived from the non-invasive OCT as a potential retinal fingerprint for CVD event across ethnicities and health conditions. TRIAL REGISTRATION ISRCTN 15853192.
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Affiliation(s)
- Yanping Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Yixiong Yuan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Shiran Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Shaopeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Junyao Zhang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne. Level 7, 32 Gisborne Street, East Melbourne, VIC, 3002, Australia
| | - Xiao Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne. Level 7, 32 Gisborne Street, East Melbourne, VIC, 3002, Australia.
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne. Level 7, 32 Gisborne Street, East Melbourne, VIC, 3002, Australia.
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
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Hsia Y, Lin YY, Wang BS, Su CY, Lai YH, Hsieh YT. Prediction of Visual Impairment in Epiretinal Membrane and Feature Analysis: A Deep Learning Approach Using Optical Coherence Tomography. Asia Pac J Ophthalmol (Phila) 2023; 12:21-28. [PMID: 36706331 DOI: 10.1097/apo.0000000000000576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 09/14/2022] [Indexed: 01/28/2023] Open
Abstract
PURPOSE The aim was to develop a deep learning model for predicting the extent of visual impairment in epiretinal membrane (ERM) using optical coherence tomography (OCT) images, and to analyze the associated features. METHODS Six hundred macular OCT images from eyes with ERM and no visually significant media opacity or other retinal diseases were obtained. Those with best-corrected visual acuity ≤20/50 were classified as "profound visual impairment," while those with best-corrected visual acuity >20/50 were classified as "less visual impairment." Ninety percent of images were used as the training data set and 10% were used for testing. Two convolutional neural network models (ResNet-50 and ResNet-18) were adopted for training. The t-distributed stochastic neighbor-embedding approach was used to compare their performances. The Grad-CAM technique was used in the heat map generative phase for feature analysis. RESULTS During the model development, the training accuracy was 100% in both convolutional neural network models, while the testing accuracy was 70% and 80% for ResNet-18 and ResNet-50, respectively. The t-distributed stochastic neighbor-embedding approach found that the deeper structure (ResNet-50) had better discrimination on OCT characteristics for visual impairment than the shallower structure (ResNet-18). The heat maps indicated that the key features for visual impairment were located mostly in the inner retinal layers of the fovea and parafoveal regions. CONCLUSIONS Deep learning algorithms could assess the extent of visual impairment from OCT images in patients with ERM. Changes in inner retinal layers were found to have a greater impact on visual acuity than the outer retinal changes.
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Affiliation(s)
- Yun Hsia
- National Taiwan University Biomedical Park Hospital, Hsin-Chu
- Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-Yi Lin
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Bo-Sin Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chung-Yen Su
- Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan
| | - Ying-Hui Lai
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Medical Device Innovation & Translation Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ting Hsieh
- Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan
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Hsu SY, Chien TW, Yeh YT, Kuo SC. Citation trends in ophthalmology articles and keywords in mainland China, Hong Kong, and Taiwan since 2013 using temporal bar graphs (TBGs): Bibliometric analysis. Medicine (Baltimore) 2022; 101:e32392. [PMID: 36596033 PMCID: PMC9803441 DOI: 10.1097/md.0000000000032392] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND We selected authors from mainland China, Hong Kong, and Taiwan (CHT) to examine citation trends on articles and keywords. The existence of suitable temporal bar graphs (TBGs) for displaying citation trends is unknown. It is necessary to enhance the traditional TBGs to provide readers with more information about the citation trend. The purpose of this study was to propose an advanced TBG that can be applied to understand the most worth-reading articles by ophthalmology authors in the CHT. METHODS Using the search engine of the Web of Science core collection, we conducted bibliometric analyses to examine the article citation trends of ophthalmology authors in CHT since 2013. A total of 6695 metadata was collected from articles and review articles. Using radar plots, the Y-index, and the combining the Y-index with the CJAL scores (CJAL) scores, we could determine the dominance of publications by year, region, institute, journal, department, and author. A choropleth map, a dot plot, and a 4-quadrant radar plot were used to visualize the results. A TBG was designed and provided for readers to display citation trends on articles and keywords. RESULTS We found that the majority of publications were published in 2017 (2275), Shanghai city (935), Sun Yat-Sen University (China) (689), the international journal Ophthalmology (1399), the Department of Ophthalmology (3035), and the author Peizeng Yang (Chongqing) (65); the highest CAJL scores were also from Guangdong (2767.22), Sun Yat-Sen University (China) (2147.35), and the Ophthalmology Department (7130.96); the author Peizeng Yang (Chongqing) (170.16) had the highest CAJL; and the enhanced TBG features maximum counts and recent growth trends that are not included in traditional TBGs. CONCLUSION Using the Y-index and the CJAL score compared with research achievements of ophthalmology authors in CHT, a 4-quadrant radar plot was provided. The enhanced TBGs and the CJAL scores are recommended for future bibliographical studies.
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Affiliation(s)
- Sheng-Yao Hsu
- Department of Ophthalmology, An Nan Hospital, China Medical University, Tainan, Taiwan
- Department of Optometry, Chung Hwa University of Medical Technology, Tainan, Taiwan
| | - Tsair-Wei Chien
- Medical Research Department, Chi-Mei Medical Center, Tainan, Taiwan
| | - Yu-Tsen Yeh
- Medical School, St. George’s, University of London, UK
| | - Shu-Chun Kuo
- Department of Optometry, Chung Hwa University of Medical Technology, Tainan, Taiwan
- Department of Ophthalmology, Chi-Mei Medical Center, Yong Kang, Tainan City, Taiwan
- * Correspondence: Shu-Chun Kuo, Chi-Mei Medical Center, 901 Chung Hwa Road, Yung Kung Dist., Tainan 710, Taiwan (e-mail: )
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Puneet, Kumar R, Gupta M. Optical coherence tomography image based eye disease detection using deep convolutional neural network. Health Inf Sci Syst 2022; 10:13. [PMID: 35756852 PMCID: PMC9213631 DOI: 10.1007/s13755-022-00182-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/08/2022] [Indexed: 12/23/2022] Open
Abstract
Over the past few decades, health care industries and medical practitioners faced a lot of obstacles to diagnosing medical-related problems due to inadequate technology and availability of equipment. In the present era, computer science technologies such as IoT, Cloud Computing, Artificial Intelligence and its allied techniques, etc. play a crucial role in the identification of medical diseases, especially in the domain of Ophthalmology. Despite this, ophthalmologists have to perform the various disease diagnosis task manually which is time-consuming and the chances of error are also very high because some of the abnormalities of eye diseases possess the same symptoms. Furthermore, multiple autonomous systems also exist to categorize the diseases but their prediction rate does not accomplish state-of-art accuracy. In the proposed approach by implementing the concept of Attention, Transfer Learning with the Deep Convolution Neural Network, the model accomplished an accuracy of 97.79% and 95.6% on the training and testing data respectively. This autonomous model efficiently classifies the various oscular disorders namely Choroidal Neovascularization, Diabetic Macular Edema, Drusen from the Optical Coherence Tomography images. It may provide a realistic solution to the healthcare sector to bring down the ophthalmologist burden in the screening of Diabetic Retinopathy.
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Affiliation(s)
- Puneet
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab India
| | - Rakesh Kumar
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab India
| | - Meenu Gupta
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab India
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Teo ZL, Lee AY, Campbell P, Chan RVP, Ting DSW. Developments in Artificial Intelligence for Ophthalmology: Federated Learning. Asia Pac J Ophthalmol (Phila) 2022; 11:500-502. [PMID: 36417673 DOI: 10.1097/apo.0000000000000582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/04/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Zhen Ling Teo
- Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore
| | - Aaron Y Lee
- Department of Ophthalmology, US Roger and Angie Karalis Johnson Retina Center, University of Washington, Seattle, WA
| | - Peter Campbell
- Department of Ophthalmology, Oregon Health and Science University, Portland, OR
| | - R V Paul Chan
- Department of Ophthalmology, University of Illinois Chicago, Chicago, IL
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, Singapore
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Leshno A, Liebmann JM. The Glaucoma Suspect Problem: Ways Forward. Asia Pac J Ophthalmol (Phila) 2022; 11:503-504. [PMID: 36278943 DOI: 10.1097/apo.0000000000000564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/20/2022] [Indexed: 11/25/2022] Open
Abstract
The diagnosis of glaucoma depends upon indentification of characteristic damage to the optic nerve and retinal fiber layer. In many cases, however, clinicians find it difficult to ascertain whether glaucomatous damage is present or absent. These patients are often labeled as "glaucoma suspects," which creates a subpopulation of individuals without clear-cut disease who nonetheless must remain under surveillance. Most will never go on to develop glaucoma, yet the need for ongoing monitoring burdens clinics and health care systems. In this perspective, we illustrate possible directions and novel approaches that can be used to remedy this situation by integrating current technologies into clinical practice. In particular, we suggest that optical coherence tomography be better utilized to methodologically classify these eyes into glaucomatous and healthy categories.
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Affiliation(s)
- Ari Leshno
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, Edward S. Harkness Eye Institute, New York, NY
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, Edward S. Harkness Eye Institute, New York, NY
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Chen YG, Chang YH. Multimodal Imaging in the Diagnosis of Macular Telangiectasia Type 1. Asia Pac J Ophthalmol (Phila) 2022; 11:397. [PMID: 35131996 DOI: 10.1097/apo.0000000000000476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/01/2021] [Indexed: 11/25/2022] Open
Affiliation(s)
- Yann-Guang Chen
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
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Ara RK, Matiolański A, Dziech A, Baran R, Domin P, Wieczorkiewicz A. Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22134675. [PMID: 35808169 PMCID: PMC9269557 DOI: 10.3390/s22134675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/13/2022] [Accepted: 06/16/2022] [Indexed: 05/18/2023]
Abstract
The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.
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Affiliation(s)
- Rouhollah Kian Ara
- Institute of Telecommunications, AGH University of Science and Technology, 30-059 Krakow, Poland; (R.K.A.); (A.D.)
| | - Andrzej Matiolański
- Institute of Telecommunications, AGH University of Science and Technology, 30-059 Krakow, Poland; (R.K.A.); (A.D.)
- Correspondence:
| | - Andrzej Dziech
- Institute of Telecommunications, AGH University of Science and Technology, 30-059 Krakow, Poland; (R.K.A.); (A.D.)
| | - Remigiusz Baran
- Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, 25-314 Kielce, Poland;
| | - Paweł Domin
- Consultronix S.A., 32-083 Balice, Poland; (P.D.); (A.W.)
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Chen DK, Modi Y, Al-Aswad LA. Promoting Transparency and Standardization in Ophthalmologic Artificial Intelligence: A Call for Artificial Intelligence Model Card. Asia Pac J Ophthalmol (Phila) 2022; 11:215-218. [PMID: 35772083 DOI: 10.1097/apo.0000000000000469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Dinah K Chen
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, US
| | - Yash Modi
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, US
| | - Lama A Al-Aswad
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, US
- Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, US
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Sampson DM, Dubis AM, Chen FK, Zawadzki RJ, Sampson DD. Towards standardizing retinal optical coherence tomography angiography: a review. LIGHT, SCIENCE & APPLICATIONS 2022; 11:63. [PMID: 35304441 PMCID: PMC8933532 DOI: 10.1038/s41377-022-00740-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 05/11/2023]
Abstract
The visualization and assessment of retinal microvasculature are important in the study, diagnosis, monitoring, and guidance of treatment of ocular and systemic diseases. With the introduction of optical coherence tomography angiography (OCTA), it has become possible to visualize the retinal microvasculature volumetrically and without a contrast agent. Many lab-based and commercial clinical instruments, imaging protocols and data analysis methods and metrics, have been applied, often inconsistently, resulting in a confusing picture that represents a major barrier to progress in applying OCTA to reduce the burden of disease. Open data and software sharing, and cross-comparison and pooling of data from different studies are rare. These inabilities have impeded building the large databases of annotated OCTA images of healthy and diseased retinas that are necessary to study and define characteristics of specific conditions. This paper addresses the steps needed to standardize OCTA imaging of the human retina to address these limitations. Through review of the OCTA literature, we identify issues and inconsistencies and propose minimum standards for imaging protocols, data analysis methods, metrics, reporting of findings, and clinical practice and, where this is not possible, we identify areas that require further investigation. We hope that this paper will encourage the unification of imaging protocols in OCTA, promote transparency in the process of data collection, analysis, and reporting, and facilitate increasing the impact of OCTA on retinal healthcare delivery and life science investigations.
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Affiliation(s)
- Danuta M Sampson
- Surrey Biophotonics, Centre for Vision, Speech and Signal Processing and School of Biosciences and Medicine, The University of Surrey, Guildford, GU2 7XH, UK.
| | - Adam M Dubis
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Trust and UCL Institute of Ophthalmology, London, EC1V 2PD, UK
| | - Fred K Chen
- Centre for Ophthalmology and Visual Science (incorporating Lions Eye Institute), The University of Western Australia, Nedlands, Western Australia, 6009, Australia
- Department of Ophthalmology, Royal Perth Hospital, Perth, Western Australia, 6000, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, 3002, Australia
| | - Robert J Zawadzki
- Department of Ophthalmology & Vision Science, University of California Davis, Sacramento, CA, 95817, USA
| | - David D Sampson
- Surrey Biophotonics, Advanced Technology Institute, School of Physics and School of Biosciences and Medicine, University of Surrey, Guildford, Surrey, GU2 7XH, UK
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