1
|
Peng J, Xie X, Lu Z, Xu Y, Xie M, Luo L, Xiao H, Ye H, Chen L, Yang J, Zhang M, Zhao P, Zheng C. Generative adversarial networks synthetic optical coherence tomography images as an education tool for image diagnosis of macular diseases: a randomized trial. Front Med (Lausanne) 2024; 11:1424749. [PMID: 39050535 PMCID: PMC11266019 DOI: 10.3389/fmed.2024.1424749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 06/19/2024] [Indexed: 07/27/2024] Open
Abstract
Purpose This study aimed to evaluate the effectiveness of generative adversarial networks (GANs) in creating synthetic OCT images as an educational tool for teaching image diagnosis of macular diseases to medical students and ophthalmic residents. Methods In this randomized trial, 20 fifth-year medical students and 20 ophthalmic residents were enrolled and randomly assigned (1:1 allocation) into Group real OCT and Group GANs OCT. All participants had a pretest to assess their educational background, followed by a 30-min smartphone-based education program using GANs or real OCT images for macular disease recognition training. Two additional tests were scheduled: one 5 min after the training to assess short-term performance, and another 1 week later to assess long-term performance. Scores and time consumption were recorded and compared. After all the tests, participants completed an anonymous subjective questionnaire. Results Group GANs OCT scores increased from 80.0 (46.0 to 85.5) to 92.0 (81.0 to 95.5) 5 min after training (p < 0.001) and 92.30 ± 5.36 1 week after training (p < 0.001). Similarly, Group real OCT scores increased from 66.00 ± 19.52 to 92.90 ± 5.71 (p < 0.001), respectively. When compared between two groups, no statistically significant difference was found in test scores, score improvements, or time consumption. After training, medical students had a significantly higher score improvement than residents (p < 0.001). Conclusion The education tool using synthetic OCT images had a similar educational ability compared to that using real OCT images, which improved the interpretation ability of ophthalmic residents and medical students in both short-term and long-term performances. The smartphone-based educational tool could be widely promoted for educational applications.Clinical trial registration: https://www.chictr.org.cn, Chinese Clinical Trial Registry [No. ChiCTR 2100053195].
Collapse
Affiliation(s)
- Jie Peng
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoling Xie
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| | - Zupeng Lu
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Ophthalmology, Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Xu
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Meng Xie
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li Luo
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| | - Haodong Xiao
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongfei Ye
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li Chen
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianlong Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Mingzhi Zhang
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| | - Peiquan Zhao
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ce Zheng
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Hospital Development Strategy, China Hospital Development Institute Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
2
|
Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
Collapse
Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA.
| |
Collapse
|
3
|
Mariottoni EB, Datta S, Shigueoka LS, Jammal AA, Tavares IM, Henao R, Carin L, Medeiros FA. Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT. Ophthalmol Glaucoma 2023; 6:228-238. [PMID: 36410708 PMCID: PMC10278200 DOI: 10.1016/j.ogla.2022.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/24/2022] [Accepted: 11/09/2022] [Indexed: 05/26/2023]
Abstract
PURPOSE To develop and validate a deep learning (DL) model for detection of glaucoma progression using spectral-domain (SD)-OCT measurements of retinal nerve fiber layer (RNFL) thickness. DESIGN Retrospective cohort study. PARTICIPANTS A total of 14 034 SD-OCT scans from 816 eyes from 462 individuals. METHODS A DL convolutional neural network was trained to assess SD-OCT RNFL thickness measurements of 2 visits (a baseline and a follow-up visit) along with time between visits to predict the probability of glaucoma progression. The ground truth was defined by consensus from subjective grading by glaucoma specialists. Diagnostic performance was summarized by the area under the receiver operator characteristic curve (AUC), sensitivity, and specificity, and was compared with conventional trend-based analyses of change. Interval likelihood ratios were calculated to determine the impact of DL model results in changing the post-test probability of progression. MAIN OUTCOME MEASURES The AUC, sensitivity, and specificity of the DL model. RESULTS The DL model had an AUC of 0.938 (95% confidence interval [CI], 0.921-0.955), with sensitivity of 87.3% (95% CI, 83.6%-91.6%) and specificity of 86.4% (95% CI, 79.9%-89.6%). When matched for the same specificity, the DL model significantly outperformed trend-based analyses. Likelihood ratios for the DL model were associated with large changes in the probability of progression in the vast majority of SD-OCT tests. CONCLUSIONS A DL model was able to assess the probability of glaucomatous structural progression from SD-OCT RNFL thickness measurements. The model agreed well with expert judgments and outperformed conventional trend-based analyses of change, while also providing indication of the likely locations of change. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
Collapse
Affiliation(s)
- Eduardo B Mariottoni
- Vision, Imaging, and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, North Carolina; Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil
| | - Shounak Datta
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina
| | - Leonardo S Shigueoka
- Vision, Imaging, and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, North Carolina
| | - Alessandro A Jammal
- Vision, Imaging, and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, North Carolina
| | - Ivan M Tavares
- Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil
| | - Ricardo Henao
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina
| | - Lawrence Carin
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina
| | - Felipe A Medeiros
- Vision, Imaging, and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, North Carolina; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
| |
Collapse
|
4
|
Dolar-Szczasny J, Barańska A, Rejdak R. Evaluating the Efficacy of Teleophthalmology in Delivering Ophthalmic Care to Underserved Populations: A Literature Review. J Clin Med 2023; 12:jcm12093161. [PMID: 37176602 PMCID: PMC10179149 DOI: 10.3390/jcm12093161] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
Technological advancement has brought commendable changes in medicine, advancing diagnosis, treatment, and interventions. Telemedicine has been adopted by various subspecialties including ophthalmology. Over the years, teleophthalmology has been implemented in various countries, and continuous progress is being made in this area. In underserved populations, due to socioeconomic factors, there is little or no access to healthcare facilities, and people are at higher risk of eye diseases and vision impairment. Transportation is the major hurdle for these people in obtaining access to eye care in the main hospitals. There is a dire need for accessible eye care for such populations, and teleophthalmology is the ray of hope for providing eye care facilities to underserved people. Numerous studies have reported the advantages of teleophthalmology for rural populations such as being cost-effective, timesaving, reliable, efficient, and satisfactory for patients. Although it is being practiced in urban populations, for rural populations, its benefits amplify. However, there are certain obstacles as well, such as the cost of equipment, lack of steady electricity and internet supply in rural areas, and the attitude of people in certain regions toward acceptance of teleophthalmology. In this review, we have discussed in detail eye health in rural populations, teleophthalmology, and its effectiveness in rural populations of different countries.
Collapse
Affiliation(s)
- Joanna Dolar-Szczasny
- Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, 20-079 Lublin, Poland
| | - Agnieszka Barańska
- Department of Medical Informatics and Statistics with E-Learning Laboratory, Medical University of Lublin, 20-090 Lublin, Poland
| | - Robert Rejdak
- Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, 20-079 Lublin, Poland
| |
Collapse
|
5
|
Hao L, Hu Y, Xu Y, Fu H, Miao H, Zheng C, Liu J. Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos. EYE AND VISION 2022; 9:41. [PMID: 36333758 PMCID: PMC9636810 DOI: 10.1186/s40662-022-00314-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022]
Abstract
Background To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance.
Methods A total of 369 AS-OCT videos (19,940 frames)—159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)—were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist. A temporal network was then developed to learn discriminative temporal features from the videos. The datasets were randomly split into training, and test sets and fivefold stratified cross-validation were used to evaluate the performance. Results For dynamic clinical parameter evaluation, the mean velocity of pupil constriction (VPC) was significantly lower in angle-closure eyes (0.470 mm/s) than in normal eyes (0.571 mm/s) (P < 0.001), as was the acceleration of pupil constriction (APC, 3.512 mm/s2vs. 5.256 mm/s2; P < 0.001). For our temporal network, the areas under the curve of the system using AS-OCT images, original AS-OCT videos, and aligned AS-OCT videos were 0.766 (95% CI: 0.610–0.923) vs. 0.820 (95% CI: 0.680–0.961) vs. 0.905 (95% CI: 0.802–1.000) (for Casia dataset) and 0.767 (95% CI: 0.620–0.914) vs. 0.837 (95% CI: 0.713–0.961) vs. 0.919 (95% CI: 0.831–1.000) (for Zeiss dataset). Conclusions The results showed, comparatively, that the iris of angle-closure eyes stretches less in response to illumination than in normal eyes. Furthermore, the dynamic feature of iris motion could assist in angle-closure classification. Supplementary Information The online version contains supplementary material available at 10.1186/s40662-022-00314-1.
Collapse
|
6
|
Thompson AC, Falconi A, Sappington RM. Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging. FRONTIERS IN OPHTHALMOLOGY 2022; 2:937205. [PMID: 38983522 PMCID: PMC11182271 DOI: 10.3389/fopht.2022.937205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/22/2022] [Indexed: 07/11/2024]
Abstract
Glaucoma is a leading cause of progressive blindness and visual impairment worldwide. Microstructural evidence of glaucomatous damage to the optic nerve head and associated tissues can be visualized using optical coherence tomography (OCT). In recent years, development of novel deep learning (DL) algorithms has led to innovative advances and improvements in automated detection of glaucomatous damage and progression on OCT imaging. DL algorithms have also been trained utilizing OCT data to improve detection of glaucomatous damage on fundus photography, thus improving the potential utility of color photos which can be more easily collected in a wider range of clinical and screening settings. This review highlights ten years of contributions to glaucoma detection through advances in deep learning models trained utilizing OCT structural data and posits future directions for translation of these discoveries into the field of aging and the basic sciences.
Collapse
Affiliation(s)
- Atalie C. Thompson
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston Salem, NC, United States
- Department of Internal Medicine, Gerontology, and Geriatric Medicine, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Aurelio Falconi
- Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Rebecca M. Sappington
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston Salem, NC, United States
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC, United States
| |
Collapse
|
7
|
WU JOHSUAN, NISHIDA TAKASHI, WEINREB ROBERTN, LIN JOUWEI. Performances of Machine Learning in Detecting Glaucoma Using Fundus and Retinal Optical Coherence Tomography Images: A Meta-Analysis. Am J Ophthalmol 2022; 237:1-12. [PMID: 34942113 DOI: 10.1016/j.ajo.2021.12.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/24/2021] [Accepted: 12/03/2021] [Indexed: 11/01/2022]
Abstract
PURPOSE To evaluate the performance of machine learning (ML) in detecting glaucoma using fundus and retinal optical coherence tomography (OCT) images. DESIGN Meta-analysis. METHODS PubMed and EMBASE were searched on August 11, 2021. A bivariate random-effects model was used to pool ML's diagnostic sensitivity, specificity, and area under the curve (AUC). Subgroup analyses were performed based on ML classifier categories and dataset types. RESULTS One hundred and five studies (3.3%) were retrieved. Seventy-three (69.5%), 30 (28.6%), and 2 (1.9%) studies tested ML using fundus, OCT, and both image types, respectively. Total testing data numbers were 197,174 for fundus and 16,039 for OCT. Overall, ML showed excellent performances for both fundus (pooled sensitivity = 0.92 [95% CI, 0.91-0.93]; specificity = 0.93 [95% CI, 0.91-0.94]; and AUC = 0.97 [95% CI, 0.95-0.98]) and OCT (pooled sensitivity = 0.90 [95% CI, 0.86-0.92]; specificity = 0.91 [95% CI, 0.89-0.92]; and AUC = 0.96 [95% CI, 0.93-0.97]). ML performed similarly using all data and external data for fundus and the external test result of OCT was less robust (AUC = 0.87). When comparing different classifier categories, although support vector machine showed the highest performance (pooled sensitivity, specificity, and AUC ranges, 0.92-0.96, 0.95-0.97, and 0.96-0.99, respectively), results by neural network and others were still good (pooled sensitivity, specificity, and AUC ranges, 0.88-0.93, 0.90-0.93, 0.95-0.97, respectively). When analyzed based on dataset types, ML demonstrated consistent performances on clinical datasets (fundus AUC = 0.98 [95% CI, 0.97-0.99] and OCT AUC = 0.95 [95% 0.93-0.97]). CONCLUSIONS Performance of ML in detecting glaucoma compares favorably to that of experts and is promising for clinical application. Future prospective studies are needed to better evaluate its real-world utility.
Collapse
|
8
|
Chaurasia AK, Greatbatch CJ, Hewitt AW. Diagnostic Accuracy of Artificial Intelligence in Glaucoma Screening and Clinical Practice. J Glaucoma 2022; 31:285-299. [PMID: 35302538 DOI: 10.1097/ijg.0000000000002015] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 02/26/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE Artificial intelligence (AI) has been shown as a diagnostic tool for glaucoma detection through imaging modalities. However, these tools are yet to be deployed into clinical practice. This meta-analysis determined overall AI performance for glaucoma diagnosis and identified potential factors affecting their implementation. METHODS We searched databases (Embase, Medline, Web of Science, and Scopus) for studies that developed or investigated the use of AI for glaucoma detection using fundus and optical coherence tomography (OCT) images. A bivariate random-effects model was used to determine the summary estimates for diagnostic outcomes. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy (PRISMA-DTA) extension was followed, and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used for bias and applicability assessment. RESULTS Seventy-nine articles met inclusion criteria, with a subset of 66 containing adequate data for quantitative analysis. The pooled area under receiver operating characteristic curve across all studies for glaucoma detection was 96.3%, with a sensitivity of 92.0% (95% confidence interval: 89.0-94.0) and specificity of 94.0% (95% confidence interval: 92.0-95.0). The pooled area under receiver operating characteristic curve on fundus and OCT images was 96.2% and 96.0%, respectively. Mixed data set and external data validation had unsatisfactory diagnostic outcomes. CONCLUSION Although AI has the potential to revolutionize glaucoma care, this meta-analysis highlights that before such algorithms can be implemented into clinical care, a number of issues need to be addressed. With substantial heterogeneity across studies, many factors were found to affect the diagnostic performance. We recommend implementing a standard diagnostic protocol for grading, implementing external data validation, and analysis across different ethnicity groups.
Collapse
Affiliation(s)
- Abadh K Chaurasia
- Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Tasmania
| | - Connor J Greatbatch
- Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Tasmania
| | - Alex W Hewitt
- Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Tasmania
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| |
Collapse
|
9
|
Yuen J, Pike S, Khachikyan S, Nallasamy S. Telehealth in Ophthalmology. Digit Health 2022. [DOI: 10.36255/exon-publications-digital-health-telehealth-ophthalmology] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
|
10
|
Kim HE, Cosa-Linan A, Santhanam N, Jannesari M, Maros ME, Ganslandt T. Transfer learning for medical image classification: a literature review. BMC Med Imaging 2022; 22:69. [PMID: 35418051 PMCID: PMC9007400 DOI: 10.1186/s12880-022-00793-7] [Citation(s) in RCA: 89] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 03/30/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task. METHODS 425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch. RESULTS The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models. CONCLUSION The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power.
Collapse
Affiliation(s)
- Hee E Kim
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
| | - Alejandro Cosa-Linan
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Nandhini Santhanam
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Mahboubeh Jannesari
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Mate E Maros
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Thomas Ganslandt
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 15, 91058, Erlangen, Germany
| |
Collapse
|
11
|
Kako NA, Abdulazeez AM. Peripapillary Atrophy Segmentation and Classification Methodologies for Glaucoma Image Detection: A Review. Curr Med Imaging 2022; 18:1140-1159. [PMID: 35260060 DOI: 10.2174/1573405618666220308112732] [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: 11/15/2021] [Revised: 12/04/2021] [Accepted: 12/22/2021] [Indexed: 11/22/2022]
Abstract
Information-based image processing and computer vision methods are utilized in several healthcare organizations to diagnose diseases. The irregularities in the visual system are identified over fundus images shaped over a fundus camera. Among ophthalmology diseases, glaucoma is measured as the most common case that can lead to neurodegenerative illness. The unsuitable fluid pressure inside the eye within the visual system is described as the major cause of those diseases. Glaucoma has no symptoms in the early stages, and if it is not treated, it may result in total blindness. Diagnosing glaucoma at an early stage may prevent permanent blindness. Manual inspection of the human eye may be a solution, but it depends on the skills of the individuals involved. The auto diagnosis of glaucoma by applying a consolidation of computer vision, artificial intelligence, and image processing can aid in the ban and detection of those diseases. In this review article, we aim to introduce a review of the numerous approaches based on peripapillary atrophy segmentation and classification that can detect these diseases, as well as details about the publicly available image benchmarks, datasets, and measurement of performance. The review article introduces the demonstrated research of numerous available study models that objectively diagnose glaucoma via peripapillary atrophy from the lowest level of feature extraction to the current direction based on deep learning. The advantages and disadvantages of each method are addressed in detail, and tabular descriptions are included to highlight the results of each category. Moreover, the frameworks of each approach and fundus image datasets are provided. The improved reporting of our study would help in providing possible future work directions to diagnose glaucoma in conclusion.
Collapse
Affiliation(s)
- Najdavan A Kako
- Duhok Polytechnic University, Technical Institute of Administration, MIS, Duhok, Iraq
| | | |
Collapse
|
12
|
Buisson M, Navel V, Labbé A, Watson SL, Baker JS, Murtagh P, Chiambaretta F, Dutheil F. Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta-analysis. Clin Exp Ophthalmol 2021; 49:1027-1038. [PMID: 34506041 DOI: 10.1111/ceo.14000] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 09/02/2021] [Accepted: 09/08/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND In this systematic review and meta-analysis, we aimed to compare deep learning versus ophthalmologists in glaucoma diagnosis on fundus examinations. METHOD PubMed, Cochrane, Embase, ClinicalTrials.gov and ScienceDirect databases were searched for studies reporting a comparison between the glaucoma diagnosis performance of deep learning and ophthalmologists on fundus examinations on the same datasets, until 10 December 2020. Studies had to report an area under the receiver operating characteristics (AUC) with SD or enough data to generate one. RESULTS We included six studies in our meta-analysis. There was no difference in AUC between ophthalmologists (AUC = 82.0, 95% confidence intervals [CI] 65.4-98.6) and deep learning (97.0, 89.4-104.5). There was also no difference using several pessimistic and optimistic variants of our meta-analysis: the best (82.2, 60.0-104.3) or worst (77.7, 53.1-102.3) ophthalmologists versus the best (97.1, 89.5-104.7) or worst (97.1, 88.5-105.6) deep learning of each study. We did not retrieve any factors influencing those results. CONCLUSION Deep learning had similar performance compared to ophthalmologists in glaucoma diagnosis from fundus examinations. Further studies should evaluate deep learning in clinical situations.
Collapse
Affiliation(s)
- Mathieu Buisson
- CHU Clermont-Ferrand, Ophthalmology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France
| | - Valentin Navel
- CHU Clermont-Ferrand, Ophthalmology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.,CNRS UMR 6293, INSERM U1103, Genetic Reproduction and Development Laboratory (GReD), Translational Approach to Epithelial Injury and Repair Team, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Antoine Labbé
- Department of Ophthalmology III, Quinze-Vingts National Ophthalmology Hospital, IHU FOReSIGHT, Paris, France.,Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.,Department of Ophthalmology, Ambroise Paré Hospital, APHP, Université de Versailles Saint-Quentin en Yvelines, Versailles, France
| | - Stephanie L Watson
- Save Sight Institute, Discipline of Ophthalmology, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Corneal Unit, Sydney Eye Hospital, Sydney, New South Wales, Australia
| | - Julien S Baker
- Centre for Health and Exercise Science Research, Department of Sport, Physical Education and Health, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Patrick Murtagh
- Department of Ophthalmology, Royal Victoria Eye and Ear Hospital, Dublin, Ireland
| | - Frédéric Chiambaretta
- CHU Clermont-Ferrand, Ophthalmology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.,CNRS UMR 6293, INSERM U1103, Genetic Reproduction and Development Laboratory (GReD), Translational Approach to Epithelial Injury and Repair Team, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Frédéric Dutheil
- Université Clermont Auvergne, CNRS, LaPSCo, Physiological and Psychosocial Stress, CHU Clermont-Ferrand, University Hospital of Clermont-Ferrand, Preventive and Occupational Medicine, Witty Fit, Clermont-Ferrand, France
| |
Collapse
|
13
|
Wong SH, Tsai JC. Telehealth and Screening Strategies in the Diagnosis and Management of Glaucoma. J Clin Med 2021; 10:jcm10163452. [PMID: 34441748 PMCID: PMC8396962 DOI: 10.3390/jcm10163452] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/31/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
Telehealth has become a viable option for glaucoma screening and glaucoma monitoring due to advances in technology. The ability to measure intraocular pressure without an anesthetic and to take optic nerve photographs without pharmacologic pupillary dilation using portable equipment have allowed glaucoma screening programs to generate enough data for assessment. At home, patients can perform visual acuity testing, web-based visual field testing, rebound tonometry, and video visits with the physician to monitor for glaucomatous progression. Artificial intelligence will enhance the accuracy of data interpretation and inspire confidence in popularizing telehealth for glaucoma.
Collapse
|
14
|
Zheng C, Koh V, Bian F, Li L, Xie X, Wang Z, Yang J, Chew PTK, Zhang M. Semi-supervised generative adversarial networks for closed-angle detection on anterior segment optical coherence tomography images: an empirical study with a small training dataset. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1073. [PMID: 34422985 PMCID: PMC8339863 DOI: 10.21037/atm-20-7436] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/17/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Semi-supervised learning algorithms can leverage an unlabeled dataset when labeling is limited or expensive to obtain. In the current study, we developed and evaluated a semi-supervised generative adversarial networks (GANs) model that detects closed-angle on anterior segment optical coherence tomography (AS-OCT) images using a small labeled dataset. METHODS In this cross-sectional study, a semi-supervised GANs model was developed for automatic closed-angle detection training on a small labeled and large unsupervised training dataset collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong (JSIEC). The closed-angle was defined as iris-trabecular contact beyond the scleral spur in AS-OCT images. We further developed two supervised deep learning (DL) models training on the same supervised dataset and the whole dataset separately. The semi-supervised GANs model and supervised DL models' performance were compared on two independent testing datasets from JSIEC (515 images) and the Department of Ophthalmology (84 images), National University Health System, respectively. The diagnostic performance was assessed by evaluation matrices, including the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS For closed-angle detection using clinician grading of AS-OCT imaging as the reference standard, the semi-supervised GANs model showed comparable performance, with AUCs of 0.97 (95% CI, 0.96-0.99) and 0.98 (95% CI, 0.94-1.00), compared with the supervised DL model (using the whole dataset) [AUCs of 0.97 (95% CI, 0.96-0.99), and 0.97 (95% CI, 0.94-1.00)]. When training on the same small supervised dataset, the semi-supervised GANs achieved performance at least as well as, if not better than, the supervised DL model [AUCs of 0.90 (95% CI: 0.84-0.96), and 0.92 (95% CI, 0.86-0.97)]. CONCLUSIONS The semi-supervised GANs method achieves diagnostic performance at least as good as a supervised DL model when trained on small labeled datasets. Further development of semi-supervised learning methods could be useful within clinical and research settings. TRIAL REGISTRATION NUMBER ChiCTR2000037892.
Collapse
Affiliation(s)
- Ce Zheng
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, Shanghai, China
| | - Victor Koh
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Fang Bian
- Department of Ophthalmology, Deyang People’s Hospital, Deyang, China
| | - Luo Li
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| | - Xiaolin Xie
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| | - Zilei Wang
- Shanghai Children’s Hospital, Shanghai, China
| | - Jianlong Yang
- Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China
| | - Paul Tec Kuan Chew
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Mingzhi Zhang
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| |
Collapse
|
15
|
Xie X, Chen B, Yang J, Huang C, Qiu K, Zheng C, Zhang M. Determinants of peripapillary retinal nerve fiber layer's grayscale value in normal eyes by spectral domain optical coherence tomography. Sci Rep 2021; 11:9577. [PMID: 33953227 PMCID: PMC8100177 DOI: 10.1038/s41598-021-88604-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 04/05/2021] [Indexed: 02/05/2023] Open
Abstract
To determine and evaluate the distribution, variation, and determinants of peripapillary retinal nerve fiber layer (pRNFL) grayscale value with spectral-domain optical coherence tomography (SD-OCT) in normal eyes. In this cross-sectional study, three hundred ninety-seven normal eyes from 397 healthy Chinese adults aged 18-80 were consecutively recruited from a tertiary eye care center. An SD-OCT instrument took pRNFL imaging. We used a customized software to measure pRNFL parameters, including thickness and grayscale value. Univariable and multiple linear regression analyses were performed to examine the relationship between pRNFL grayscale value with ocular (e.g., axial length [A.L.], spherical equivalent [S.E.], intraocular pressure [IOP]), and systemic (e.g., age, sex) factors. A total of 397 eyes from 397 healthy subjects were included in the final analysis with mean (± SD) age 44.63 ± 16.43 years (range 18-80 years) and 196 (49.4%) males. The mean average of pRNFL grayscale value and thickness 164.82 ± 5.69 and 106.68 ± 8.89 μm, respectively. pRNFL grayscale value in nasal sectors (163.26 ± 9.31) was significantly lower comparing those in all other five sectors (all with p < 0.001)]. In multivariable analysis, average pRNFL grayscale value was independently correlated to older age (β = - 0.053, p = 0.002), longer axial length (β = - 0.664, p = 0.003), lower RPE grayscale value (β = 0.372, p < 0.001) and lower ImageQ (β = 0.658, p < 0.001). In this study, we provided normative SD-OCT data on the pRNFL grayscale value profile in nonglaucomatous eyes. Lower average pRNFL grayscale value was independently correlated to older age, longer axial length, lower RPE grayscale value, and lower ImageQ. These determinants should be considered when interpreting pRNFL grayscale value in glaucoma assessment.
Collapse
Affiliation(s)
- Xiaolin Xie
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China
| | - Binyao Chen
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China
| | - Jianling Yang
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China
| | - Chukai Huang
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China
| | - Kunliang Qiu
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China
| | - Ce Zheng
- Department of Ophthalmology, Xinhua Hospital, Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
| | - Mingzhi Zhang
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China.
| |
Collapse
|
16
|
Li JPO, Liu H, Ting DSJ, Jeon S, Chan RVP, Kim JE, Sim DA, Thomas PBM, Lin H, Chen Y, Sakomoto T, Loewenstein A, Lam DSC, Pasquale LR, Wong TY, Lam LA, Ting DSW. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Prog Retin Eye Res 2021; 82:100900. [PMID: 32898686 PMCID: PMC7474840 DOI: 10.1016/j.preteyeres.2020.100900] [Citation(s) in RCA: 201] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/25/2020] [Accepted: 08/31/2020] [Indexed: 12/29/2022]
Abstract
The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.
Collapse
Affiliation(s)
- Ji-Peng Olivia Li
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Hanruo Liu
- Beijing Tongren Hospital; Capital Medical University; Beijing Institute of Ophthalmology; Beijing, China
| | - Darren S J Ting
- Academic Ophthalmology, University of Nottingham, United Kingdom
| | - Sohee Jeon
- Keye Eye Center, Seoul, Republic of Korea
| | | | - Judy E Kim
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dawn A Sim
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Peter B M Thomas
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Haotian Lin
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Guangzhou, China
| | - Youxin Chen
- Peking Union Medical College Hospital, Beijing, China
| | - Taiji Sakomoto
- Department of Ophthalmology, Kagoshima University Graduate School of Medical and Dental Sciences, Japan
| | | | - Dennis S C Lam
- C-MER Dennis Lam Eye Center, C-Mer International Eye Care Group Limited, Hong Kong, Hong Kong; International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Tien Y Wong
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore
| | - Linda A Lam
- USC Roski Eye Institute, University of Southern California (USC) Keck School of Medicine, Los Angeles, CA, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore.
| |
Collapse
|
17
|
Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, Ashrafian H, Darzi A. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med 2021; 4:65. [PMID: 33828217 PMCID: PMC8027892 DOI: 10.1038/s41746-021-00438-z] [Citation(s) in RCA: 229] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/25/2021] [Indexed: 12/19/2022] Open
Abstract
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC's ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC's ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC's ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.
Collapse
Affiliation(s)
- Ravi Aggarwal
- Institute of Global Health Innovation, Imperial College London, London, UK
| | | | - Guy Martin
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | | | - Dominic King
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London, UK.
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London, UK
| |
Collapse
|
18
|
Aksoy M, Toptan M, An I. Retinal nerve fibre layer thickness and choroidal thickness: An evaluation in psoriasis patients. Int J Clin Pract 2021; 75:e13904. [PMID: 33290620 DOI: 10.1111/ijcp.13904] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/30/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND/OBJECTIVES To conduct a comparative study of retinal nerve fibre layer (RNFL) thickness and choroidal thickness of psoriasis patients and healthy volunteers. METHODS This study included 35 severe psoriasis patients, 35 mild psoriasis patients and 35 healthy individuals. RNFL and choroidal thickness analysis were performed by spectral field optical coherence tomography (SD-OCT). Only patients with psoriasis vulgaris who have not used systemic therapy for the last 3 months were included in the study. RESULTS In the severe psoriasis group, the RSLT thickness was found to be statistically significantly thinner and the choroid thickness was thicker than the mild psoriasis and control group (P < .001). There was no significant difference in terms of RNLF and choroid thickness compared to mild psoriasis and the control group (P > .05). The correlation between length of the disease duration, RNFL and choroidal thickness was not significant (P > 0,05). CONCLUSION The increase in choroidal thickness was found to be significant, while with respect to RNFL thickness, a decrease was evident, a possible indicator of damage to microvascular structures in the choroid and ganglion cells, especially in patients with severe psoriasis. Therefore, choroidal and RSLT thickness measurement with OCT device can assist in the detection of damage to psoriasis.
Collapse
Affiliation(s)
- Mustafa Aksoy
- Department of Dermatolog, Harran University Medical Faculty, Sanlıurfa, Turkey
| | - Muslum Toptan
- Department of Ophthalmotology, Harran University Medical Faculty, Sanlıurfa, Turkey
| | - Isa An
- Department of Dermatolog, Sanlıurfa Training and Research Hospital, Sanlıurfa, Turkey
| |
Collapse
|
19
|
Mirzania D, Thompson AC, Muir KW. Applications of deep learning in detection of glaucoma: A systematic review. Eur J Ophthalmol 2020; 31:1618-1642. [PMID: 33274641 DOI: 10.1177/1120672120977346] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical practice. Artificial intelligence (AI) is an expanding field that offers the potential to improve diagnosis and screening for glaucoma with minimal reliance on human input. Deep learning (DL) algorithms have risen to the forefront of AI by providing nearly human-level performance, at times exceeding the performance of humans for detection of glaucoma on structural and functional tests. A succinct summary of present studies and challenges to be addressed in this field is needed. Following PRISMA guidelines, we conducted a systematic review of studies that applied DL methods for detection of glaucoma using color fundus photographs, optical coherence tomography (OCT), or standard automated perimetry (SAP). In this review article we describe recent advances in DL as applied to the diagnosis of glaucoma and glaucoma progression for application in screening and clinical settings, as well as the challenges that remain when applying this novel technique in glaucoma.
Collapse
Affiliation(s)
| | - Atalie C Thompson
- Duke University School of Medicine, Durham, NC, USA.,Durham VA Medical Center, Durham, NC, USA
| | - Kelly W Muir
- Duke University School of Medicine, Durham, NC, USA.,Durham VA Medical Center, Durham, NC, USA
| |
Collapse
|