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Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, Wong TY. Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmol Ther 2024; 13:2125-2149. [PMID: 38913289 PMCID: PMC11246322 DOI: 10.1007/s40123-024-00981-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/15/2024] [Indexed: 06/25/2024] Open
Abstract
We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.
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Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań , Poland.
| | - Kai Jin
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingxin Zhou
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiangji Pan
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Meizhu Wang
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Juan Ye
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Tien Y Wong
- School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
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Zhang X, Zhao J, Li Y, Wu H, Zhou X, Liu J. Efficient pyramid channel attention network for pathological myopia recognition with pretraining-and-finetuning. Artif Intell Med 2024; 154:102926. [PMID: 38964193 DOI: 10.1016/j.artmed.2024.102926] [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: 01/08/2024] [Revised: 06/21/2024] [Accepted: 06/25/2024] [Indexed: 07/06/2024]
Abstract
Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristics of pathology distribution in PM are global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime.
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Affiliation(s)
- Xiaoqing Zhang
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Jilu Zhao
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yan Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Hao Wu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Xiangtian Zhou
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China; Research Unit of Myopia Basic Research and Clinical Prevention and Control, Chinese Academy of Medical Sciences, Wenzhou, 325027, China
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China; Singapore Eye Research Institute, 169856, Singapore.
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Chiang YY, Chen CL, Chen YH. Deep Learning Evaluation of Glaucoma Detection Using Fundus Photographs in Highly Myopic Populations. Biomedicines 2024; 12:1394. [PMID: 39061968 PMCID: PMC11274657 DOI: 10.3390/biomedicines12071394] [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: 04/28/2024] [Revised: 06/14/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
OBJECTIVES This study aimed to use deep learning to identify glaucoma and normal eyes in groups with high myopia using fundus photographs. METHODS Patients who visited Tri-Services General Hospital from 1 November 2018 to 31 October 2022 were retrospectively reviewed. Patients with high myopia (spherical equivalent refraction of ≤-6.0 D) were included in the current analysis. Meanwhile, patients with pathological myopia were excluded. The participants were then divided into the high myopia group and high myopia glaucoma group. We used two classification models with the convolutional block attention module (CBAM), an attention mechanism module that enhances the performance of convolutional neural networks (CNNs), to investigate glaucoma cases. The learning data of this experiment were evaluated through fivefold cross-validation. The images were categorized into training, validation, and test sets in a ratio of 6:2:2. Grad-CAM visual visualization improved the interpretability of the CNN results. The performance indicators for evaluating the model include the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS A total of 3088 fundus photographs were used for the deep-learning model, including 1540 and 1548 fundus photographs for the high myopia glaucoma and high myopia groups, respectively. The average refractive power of the high myopia glaucoma group and the high myopia group were -8.83 ± 2.9 D and -8.73 ± 2.6 D, respectively (p = 0.30). Based on a fivefold cross-validation assessment, the ConvNeXt_Base+CBAM architecture had the best performance, with an AUC of 0.894, accuracy of 82.16%, sensitivity of 81.04%, specificity of 83.27%, and F1 score of 81.92%. CONCLUSIONS Glaucoma in individuals with high myopia was identified from their fundus photographs.
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Affiliation(s)
- Yen-Ying Chiang
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan;
| | - Ching-Long Chen
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
| | - Yi-Hao Chen
- Department of Ophthalmology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan;
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Guo Y, Liu Y, Hu Z, Li Y, Zhang H, Zhao S. Efficacy and safety of 0.01% atropine combined with orthokeratology lens in delaying juvenile myopia: An observational study. Medicine (Baltimore) 2024; 103:e38384. [PMID: 38875374 PMCID: PMC11175863 DOI: 10.1097/md.0000000000038384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 03/05/2024] [Accepted: 05/07/2024] [Indexed: 06/16/2024] Open
Abstract
It aims to study the efficacy and safety of low-concentration Atropine combined with orthokeratology (OK) lens in delaying juvenile myopia. This is a prospective study, 172 adolescents aged 8 to 12 years who were admitted to the diopter department of Hengshui People Hospital from April 2021 to May 2022 were selected. According to the equivalent spherical diopter measured at the time of initial diagnosis, myopic patients were randomly divided into low myopia group (group A) and moderate myopia group (group B). At the same time, according to the different treatment methods, the patients were divided into the group wearing frame glasses alone (group c), the group wearing frame glasses with low-concentration Atropine (group d), the group wearing corneal shaping glasses alone at night (group e), and the group wearing corneal shaping glasses at night with low-concentration Atropine (group f). The control effect of myopia development and axial elongation in group f was better than that in groups d and e (P < .05). The effect of controlling myopia development and axial elongation in group f is with P > .05. The probability of postoperative adverse reactions in group f was lower and lower than that in the other groups. Low-concentration atropine combined with OK lens could effectively delay the development of juvenile myopia, and had a high safety. Low-concentration of Atropine would not have a significant impact on the basic tear secretion and tear film stability. Nightwear of OK lens also had no significant impact, but it would significantly reduce the tear film rupture time in the first 3 months, and at the same time, the tear film rupture time would be the same after 6 months as before treatment.
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Affiliation(s)
- YanFang Guo
- Department of Ophthalmology, Hengshui People’s Hospital, Hengshui, China
| | - Ying Liu
- Department of Ophthalmology, Hengshui People’s Hospital, Hengshui, China
| | - ZhiWei Hu
- Department of Stomatology, Hengshui People’s Hospital, Hengshui, China
| | - YueFeng Li
- Department of Ophthalmology, Hengshui People’s Hospital, Hengshui, China
| | - HePeng Zhang
- Department of Ophthalmology, Hengshui People’s Hospital, Hengshui, China
| | - SuYan Zhao
- Department of Ophthalmology, Hengshui People’s Hospital, Hengshui, China
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Ma Y, He J, Tan D, Han X, Feng R, Xiong H, Peng X, Pu X, Zhang L, Li Y, Chen S. The clinical and imaging data fusion model for single-period cerebral CTA collateral circulation assessment. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024:XST240083. [PMID: 38820061 DOI: 10.3233/xst-240083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
Background The Chinese population ranks among the highest globally in terms of stroke prevalence. In the clinical diagnostic process, radiologists utilize computed tomography angiography (CTA) images for diagnosis, enabling a precise assessment of collateral circulation in the brains of stroke patients. Recent studies frequently combine imaging and machine learning methods to develop computer-aided diagnostic algorithms. However, in studies concerning collateral circulation assessment, the extracted imaging features are primarily composed of manually designed statistical features, which exhibit significant limitations in their representational capacity. Accurately assessing collateral circulation using image features in brain CTA images still presents challenges. Methods To tackle this issue, considering the scarcity of publicly accessible medical datasets, we combined clinical data with imaging data to establish a dataset named RadiomicsClinicCTA. Moreover, we devised two collateral circulation assessment models to exploit the synergistic potential of patients' clinical information and imaging data for a more accurate assessment of collateral circulation: data-level fusion and feature-level fusion. To remove redundant features from the dataset, we employed Levene's test and T-test methods for feature pre-screening. Subsequently, we performed feature dimensionality reduction using the LASSO and random forest algorithms and trained classification models with various machine learning algorithms on the data-level fusion dataset after feature engineering. Results Experimental results on the RadiomicsClinicCTA dataset demonstrate that the optimized data-level fusion model achieves an accuracy and AUC value exceeding 86% . Subsequently, we trained and assessed the performance of the feature-level fusion classification model. The results indicate the feature-level fusion classification model outperforms the optimized data-level fusion model. Comparative experiments show that the fused dataset better differentiates between good and bad side branch features relative to the pure radiomics dataset. Conclusions Our study underscores the efficacy of integrating clinical and imaging data through fusion models, significantly enhancing the accuracy of collateral circulation assessment in stroke patients.
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Affiliation(s)
- Yuqi Ma
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Jingliu He
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Duo Tan
- The Second People's Hospital of Guizhou Province, Guizhou, China
| | - Xu Han
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Ruiqi Feng
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Hailing Xiong
- College of Electronic and Information Engineering, Southwest University, Chongqing, China
| | - Xihua Peng
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Xun Pu
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Lin Zhang
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shanxiong Chen
- College of Computer and Information Science, Southwest University, Chongqing, China
- Big Data & Intelligence Engineering School, Chongqing College of International Business and Economics, Chongqing, China
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Driban M, Yan A, Selvam A, Ong J, Vupparaboina KK, Chhablani J. Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review. Int J Retina Vitreous 2024; 10:36. [PMID: 38654344 PMCID: PMC11036694 DOI: 10.1186/s40942-024-00554-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Applications for artificial intelligence (AI) in ophthalmology are continually evolving. Fundoscopy is one of the oldest ocular imaging techniques but remains a mainstay in posterior segment imaging due to its prevalence, ease of use, and ongoing technological advancement. AI has been leveraged for fundoscopy to accomplish core tasks including segmentation, classification, and prediction. MAIN BODY In this article we provide a review of AI in fundoscopy applied to representative chorioretinal pathologies, including diabetic retinopathy and age-related macular degeneration, among others. We conclude with a discussion of future directions and current limitations. SHORT CONCLUSION As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.
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Affiliation(s)
- Matthew Driban
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Audrey Yan
- Department of Medicine, West Virginia School of Osteopathic Medicine, Lewisburg, WV, USA
| | - Amrish Selvam
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, USA
| | | | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Prashar J, Tay N. Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis. Eye (Lond) 2024; 38:303-314. [PMID: 37550366 PMCID: PMC10810874 DOI: 10.1038/s41433-023-02680-z] [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: 12/21/2022] [Revised: 07/12/2023] [Accepted: 07/19/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Pathological myopia (PM) is a major cause of worldwide blindness and represents a serious threat to eye health globally. Artificial intelligence (AI)-based methods are gaining traction in ophthalmology as highly sensitive and specific tools for screening and diagnosis of many eye diseases. However, there is currently a lack of high-quality evidence for their use in the diagnosis of PM. METHODS A systematic review and meta-analysis of studies evaluating the diagnostic performance of AI-based tools in PM was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance. Five electronic databases were searched, results were assessed against the inclusion criteria and a quality assessment was conducted for included studies. Model sensitivity and specificity were pooled using the DerSimonian and Laird (random-effects) model. Subgroup analysis and meta-regression were performed. RESULTS Of 1021 citations identified, 17 studies were included in the systematic review and 11 studies, evaluating 165,787 eyes, were included in the meta-analysis. The area under the summary receiver operator curve (SROC) was 0.9905. The pooled sensitivity was 95.9% [95.5%-96.2%], and the overall pooled specificity was 96.5% [96.3%-96.6%]. The pooled diagnostic odds ratio (DOR) for detection of PM was 841.26 [418.37-1691.61]. CONCLUSIONS This systematic review and meta-analysis provides robust early evidence that AI-based, particularly deep-learning based, diagnostic tools are a highly specific and sensitive modality for the detection of PM. There is potential for such tools to be incorporated into ophthalmic public health screening programmes, particularly in resource-poor areas with a substantial prevalence of high myopia.
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Affiliation(s)
- Jai Prashar
- University College London, London, UK.
- Moorfields Eye Hospital NHS Foundation Trust, London, UK.
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Yao Y, Yang J, Sun H, Kong H, Wang S, Xu K, Dai W, Jiang S, Bai Q, Xing S, Yuan J, Liu X, Lu F, Chen Z, Qu J, Su J. DeepGraFT: A novel semantic segmentation auxiliary ROI-based deep learning framework for effective fundus tessellation classification. Comput Biol Med 2024; 169:107881. [PMID: 38159401 DOI: 10.1016/j.compbiomed.2023.107881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/04/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024]
Abstract
Fundus tessellation (FT) is a prevalent clinical feature associated with myopia and has implications in the development of myopic maculopathy, which causes irreversible visual impairment. Accurate classification of FT in color fundus photo can help predict the disease progression and prognosis. However, the lack of precise detection and classification tools has created an unmet medical need, underscoring the importance of exploring the clinical utility of FT. Thus, to address this gap, we introduce an automatic FT grading system (called DeepGraFT) using classification-and-segmentation co-decision models by deep learning. ConvNeXt, utilizing transfer learning from pretrained ImageNet weights, was employed for the classification algorithm, aligning with a region of interest based on the ETDRS grading system to boost performance. A segmentation model was developed to detect FT exits, complementing the classification for improved grading accuracy. The training set of DeepGraFT was from our in-house cohort (MAGIC), and the validation sets consisted of the rest part of in-house cohort and an independent public cohort (UK Biobank). DeepGraFT demonstrated a high performance in the training stage and achieved an impressive accuracy in validation phase (in-house cohort: 86.85 %; public cohort: 81.50 %). Furthermore, our findings demonstrated that DeepGraFT surpasses machine learning-based classification models in FT classification, achieving a 5.57 % increase in accuracy. Ablation analysis revealed that the introduced modules significantly enhanced classification effectiveness and elevated accuracy from 79.85 % to 86.85 %. Further analysis using the results provided by DeepGraFT unveiled a significant negative association between FT and spherical equivalent (SE) in the UK Biobank cohort. In conclusion, DeepGraFT accentuates potential benefits of the deep learning model in automating the grading of FT and allows for potential utility as a clinical-decision support tool for predicting progression of pathological myopia.
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Affiliation(s)
- Yinghao Yao
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Jiaying Yang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Haojun Sun
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Hengte Kong
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Sheng Wang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Ke Xu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Wei Dai
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Siyi Jiang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - QingShi Bai
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Shilai Xing
- Institute of PSI Genomics, Wenzhou Global Eye & Vision Innovation Center, Wenzhou, 325024, China
| | - Jian Yuan
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Xinting Liu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Fan Lu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Zhenhui Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
| | - Jia Qu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
| | - Jianzhong Su
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Eye Hospital, Wenzhou Medical University, Wenzhou, 325011, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
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Fang H, Li F, Wu J, Fu H, Sun X, Orlando JI, Bogunović H, Zhang X, Xu Y. Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation. Sci Data 2024; 11:99. [PMID: 38245589 PMCID: PMC10799845 DOI: 10.1038/s41597-024-02911-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.
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Affiliation(s)
- Huihui Fang
- South China University of Technology, Guangzhou, China
- Pazhou Lab., Guangzhou, China
| | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Junde Wu
- National University of Singapore, Singapore, Singapore
| | - Huazhu Fu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore
| | - Xu Sun
- Pazhou Lab., Guangzhou, China
| | | | - Hrvoje Bogunović
- Christian Doppler Lab for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
| | - Yanwu Xu
- South China University of Technology, Guangzhou, China.
- Pazhou Lab., Guangzhou, China.
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10
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Zhang J, Zou H. Insights into artificial intelligence in myopia management: from a data perspective. Graefes Arch Clin Exp Ophthalmol 2024; 262:3-17. [PMID: 37231280 PMCID: PMC10212230 DOI: 10.1007/s00417-023-06101-5] [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: 11/26/2022] [Revised: 03/23/2023] [Accepted: 05/06/2023] [Indexed: 05/27/2023] Open
Abstract
Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.
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Affiliation(s)
- Juzhao Zhang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haidong Zou
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Eye Diseases Prevention & Treatment Center, Shanghai Eye Hospital, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
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Zheng B, Zhang M, Zhu S, Wu M, Chen L, Zhang S, Yang W. Research on an artificial intelligence-based myopic maculopathy grading method using EfficientNet. Indian J Ophthalmol 2024; 72:S53-S59. [PMID: 38131543 PMCID: PMC10833160 DOI: 10.4103/ijo.ijo_48_23] [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: 01/06/2023] [Revised: 08/04/2023] [Accepted: 08/15/2023] [Indexed: 12/23/2023] Open
Abstract
PURPOSE We aimed to develop an artificial intelligence-based myopic maculopathy grading method using EfficientNet to overcome the delayed grading and diagnosis of different myopic maculopathy degrees. METHODS The cooperative hospital provided 4642 healthy and myopic maculopathy color fundus photographs, comprising the four degrees of myopic maculopathy and healthy fundi. The myopic maculopathy grading models were trained using EfficientNet-B0 to EfficientNet-B7 models. The diagnostic results were compared with those of the VGG16 and ResNet50 classification models. The leading evaluation indicators were sensitivity, specificity, F1 score, area under the receiver operating characteristic (ROC) curve area under curve (AUC), 95% confidence interval, kappa value, and accuracy. The ROC curves of the ten grading models were also compared. RESULTS We used 1199 color fundus photographs to evaluate the myopic maculopathy grading models. The size of the EfficientNet-B0 myopic maculopathy grading model was 15.6 MB, and it had the highest kappa value (88.32%) and accuracy (83.58%). The model's sensitivities to diagnose tessellated fundus (TF), diffuse chorioretinal atrophy (DCA), patchy chorioretinal atrophy (PCA), and macular atrophy (MA) were 96.86%, 75.98%, 64.67%, and 88.75%, respectively. The specificity was above 93%, and the AUCs were 0.992, 0.960, 0.964, and 0.989, respectively. CONCLUSION The EfficientNet models were used to design grading diagnostic models for myopic maculopathy. Based on the collected fundus images, the models could diagnose a healthy fundus and four types of myopic maculopathy. The models might help ophthalmologists to make preliminary diagnoses of different degrees of myopic maculopathy.
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Affiliation(s)
- Bo Zheng
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Maotao Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Shaojun Zhu
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Maonian Wu
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Lu Chen
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | | | - Weihua Yang
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
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12
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Zhang Y, Li Y, Liu J, Wang J, Li H, Zhang J, Yu X. Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis. Eye (Lond) 2023; 37:3565-3573. [PMID: 37117783 PMCID: PMC10141825 DOI: 10.1038/s41433-023-02551-7] [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: 01/07/2023] [Revised: 03/30/2023] [Accepted: 04/18/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND/OBJECTIVE Pathologic myopia (PM) is a major cause of severe visual impairment and blindness, and current applications of artificial intelligence (AI) have covered the diagnosis and classification of PM. This meta-analysis and systematic review aimed to evaluate the overall performance of AI-based models in detecting PM and related complications. METHODS We searched PubMed, Scopus, Embase, Web of Science and IEEE Xplore for eligible studies before Dec 20, 2022. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We calculated the pooled sensitivity (SEN), specificity (SPE) and the summary area under the curve (AUC) using a random effects model, to evaluate the performance of AI in the detection of PM based on fundus or optical coherence tomography (OCT) images. RESULTS 22 studies were included in the systematic review, and 14 of them were included in the quantitative analysis. Of all included studies, SEN and SPE ranged from 80.0% to 98.7% and from 79.5% to 100.0% for PM detection, respectively. For the detection of PM, the summary AUC was 0.99 (95% confidence interval (CI) 0.97 to 0.99), and the pooled SEN and SPE were 0.95 (95% CI 0.92 to 0.96) and 0.97 (95% CI: 0.94 to 0.98), respectively. For the detection of PM-related choroid neovascularization (CNV), the summary AUC was 0.99 (95% CI: 0.97 to 0.99). CONCLUSION Our review demonstrated the excellent performance of current AI algorithms in detecting PM and related complications based on fundus and OCT images.
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Affiliation(s)
- Yue Zhang
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Yilin Li
- Center for Statistical Sciences, Peking University, Beijing, China
| | - Jing Liu
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Jianing Wang
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Hui Li
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Jinrong Zhang
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaobing Yu
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
- Graduate School of Peking Union Medical College, Beijing, China.
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Tan D, Liu J, Chen S, Yao R, Li Y, Zhu S, Li L. Automatic Evaluating of Multi-Phase Cranial CTA Collateral Circulation Based on Feature Fusion Attention Network Model. IEEE Trans Nanobioscience 2023; 22:789-799. [PMID: 37276106 DOI: 10.1109/tnb.2023.3283049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Stroke is one of the main causes of disability and death, and it can be divided into hemorrhagic stroke and ischemic stroke. Ischemic stroke is more common, and about 8 out of 10 stroke patients suffer from ischemic stroke. In clinical practice, doctors diagnose stroke by using computed tomography angiography (CTA) image to accurately evaluate the collateral circulation in stroke patients. This imaging information is of great significance in assisting doctors to determine the patient's treatment plan and prognosis. Currently, great progress has been made in the field of computer-aided diagnosis technology in medicine by using artificial intelligence. However, in related research based on deep learning algorithms, researchers usually only use single-phase data for training, lacking the temporal dimension information of multi-phase image data. This makes it difficult for the model to learn more comprehensive and effective collateral circulation feature representation, thereby limiting its performance. Therefore, combining data for training is expected to improve the accuracy and reliability of collateral circulation evaluation. In this study, we propose an effective hybrid mechanism to assist the feature encoding network in evaluating the degree of collateral circulation in the brain. By using a hybrid attention mechanism, additional guidance and regularization are provided to enhance the collateral circulation feature representation across multiple stages. Time dimension information is added to the input, and multiple feature-level fusion modules are designed in the multi-branch network. The first fusion module in the single-stage feature extraction network completes the fusion of deep and shallow vessel features in the single-branch network, followed by the multi-stage network feature fusion module, which achieves feature fusion for four stages. Tested on a dataset of multi-phase cranial CTA images, the accuracy rate exceeding 90.43%. The experimental results demonstrate that the addition of these modules can fully explore collateral vessel features, improve feature expression capabilities, and optimize the performance of deep learning network model.
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Crincoli E, Sacconi R, Querques G. Reshaping the use of Artificial Intelligence in Ophthalmology: Sometimes you Need to go Backwards. Retina 2023; 43:1429-1432. [PMID: 37343295 DOI: 10.1097/iae.0000000000003878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2023]
Affiliation(s)
- Emanuele Crincoli
- Department of Ophthalmology, University Vita-Salute, IRCCS San Raffaele Scientific Institute, Milan, Italy
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15
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Hubbard DC, Cox P, Redd TK. Assistive applications of artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2023; 34:261-266. [PMID: 36728651 PMCID: PMC10065924 DOI: 10.1097/icu.0000000000000939] [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] [Indexed: 02/03/2023]
Abstract
PURPOSE OF REVIEW Assistive (nonautonomous) artificial intelligence (AI) models designed to support (rather than function independently of) clinicians have received increasing attention in medicine. This review aims to highlight several recent developments in these models over the past year and their ophthalmic implications. RECENT FINDINGS Artificial intelligence models with a diverse range of applications in ophthalmology have been reported in the literature over the past year. Many of these systems have reported high performance in detection, classification, prognostication, and/or monitoring of retinal, glaucomatous, anterior segment, and other ocular pathologies. SUMMARY Over the past year, developments in AI have been made that have implications affecting ophthalmic surgical training and refractive outcomes after cataract surgery, therapeutic monitoring of disease, disease classification, and prognostication. Many of these recently developed models have obtained encouraging results and have the potential to serve as powerful clinical decision-making tools pending further external validation and evaluation of their generalizability.
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Affiliation(s)
- Donald C Hubbard
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Parker Cox
- Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Travis K Redd
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
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16
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Clark R, Lee SSY, Du R, Wang Y, Kneepkens SCM, Charng J, Huang Y, Hunter ML, Jiang C, Tideman JWL, Melles RB, Klaver CCW, Mackey DA, Williams C, Choquet H, Ohno-Matsui K, Guggenheim JA. A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration. EBioMedicine 2023; 91:104551. [PMID: 37055258 PMCID: PMC10203044 DOI: 10.1016/j.ebiom.2023.104551] [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: 12/08/2022] [Revised: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ -6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. METHODS The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. FINDINGS In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17-21%), 2% (1-3%), 8% (7-10%) and 6% (3-9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75-0.81), 0.58 (0.53-0.64), 0.71 (0.69-0.74) and 0.67 (0.62-0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92-1.24). INTERPRETATION Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted for. FUNDING Supported by the Welsh Government and Fight for Sight (24WG201).
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Affiliation(s)
- Rosie Clark
- School of Optometry & Vision Sciences, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Samantha Sze-Yee Lee
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia
| | - Ran Du
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan; Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Yining Wang
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan
| | - Sander C M Kneepkens
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jason Charng
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia; Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Yu Huang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Michael L Hunter
- Busselton Health Study Centre, Busselton Population Medical Research Institute, Busselton, Western Australia; School of Population and Global Health, University of Western Australia, Perth, Western Australia
| | - Chen Jiang
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - J Willem L Tideman
- Department of Ophthalmology, Martini Hospital, Groningen, the Netherlands; Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ronald B Melles
- Department of Ophthalmology Kaiser Permanente Northern California, Redwood City, CA, USA
| | - Caroline C W Klaver
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - David A Mackey
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia; Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Victoria, Australia; School of Medicine, Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, Australia
| | - Cathy Williams
- Centre for Academic Child Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS81NU, UK
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan
| | - Jeremy A Guggenheim
- School of Optometry & Vision Sciences, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
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Li Y, Yip MYT, Ting DSW, Ang M. Artificial intelligence and digital solutions for myopia. Taiwan J Ophthalmol 2023; 13:142-150. [PMID: 37484621 PMCID: PMC10361438 DOI: 10.4103/tjo.tjo-d-23-00032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 03/16/2023] [Indexed: 07/25/2023] Open
Abstract
Myopia as an uncorrected visual impairment is recognized as a global public health issue with an increasing burden on health-care systems. Moreover, high myopia increases one's risk of developing pathologic myopia, which can lead to irreversible visual impairment. Thus, increased resources are needed for the early identification of complications, timely intervention to prevent myopia progression, and treatment of complications. Emerging artificial intelligence (AI) and digital technologies may have the potential to tackle these unmet needs through automated detection for screening and risk stratification, individualized prediction, and prognostication of myopia progression. AI applications in myopia for children and adults have been developed for the detection, diagnosis, and prediction of progression. Novel AI technologies, including multimodal AI, explainable AI, federated learning, automated machine learning, and blockchain, may further improve prediction performance, safety, accessibility, and also circumvent concerns of explainability. Digital technology advancements include digital therapeutics, self-monitoring devices, virtual reality or augmented reality technology, and wearable devices - which provide possible avenues for monitoring myopia progression and control. However, there are challenges in the implementation of these technologies, which include requirements for specific infrastructure and resources, demonstrating clinically acceptable performance and safety of data management. Nonetheless, this remains an evolving field with the potential to address the growing global burden of myopia.
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Affiliation(s)
- Yong Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Michelle Y. T. Yip
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Daniel S. W. Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
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Sun Y, Li Y, Zhang F, Zhao H, Liu H, Wang N, Li H. A deep network using coarse clinical prior for myopic maculopathy grading. Comput Biol Med 2023; 154:106556. [PMID: 36682177 DOI: 10.1016/j.compbiomed.2023.106556] [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: 07/03/2022] [Revised: 12/19/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Pathological Myopia (PM) is a globally prevalent eye disease which is one of the main causes of blindness. In the long-term clinical observation, myopic maculopathy is a main criterion to diagnose PM severity. The grading of myopic maculopathy can provide a severity and progression prediction of PM to perform treatment and prevent myopia blindness in time. In this paper, we propose a feature fusion framework to utilize tessellated fundus and the brightest region in fundus images as prior knowledge. The proposed framework consists of prior knowledge extraction module and feature fusion module. Prior knowledge extraction module uses traditional image processing methods to extract the prior knowledge to indicate coarse lesion positions in fundus images. Furthermore, the prior, tessellated fundus and the brightest region in fundus images, are integrated into deep learning network as global and local constrains respectively by feature fusion module. In addition, rank loss is designed to increase the continuity of classification score. We collect a private color fundus dataset from Beijing TongRen Hospital containing 714 clinical images. The dataset contains all 5 grades of myopic maculopathy which are labeled by experienced ophthalmologists. Our framework achieves 0.8921 five-grade accuracy on our private dataset. Pathological Myopia (PALM) dataset is used for comparison with other related algorithms. Our framework is trained with 400 images and achieves an AUC of 0.9981 for two-class grading. The results show that our framework can achieve a good performance for myopic maculopathy grading.
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Affiliation(s)
- Yun Sun
- Beijing Institute of Technology, No. 5, Zhong Guan Cun South Street, Beijing, 100081, China
| | - Yu Li
- Beijing Tongren Hospital, Capital Medical University, No. 2, Chongwenmennei Street, Beijing, 100730, China
| | - Fengju Zhang
- Beijing Tongren Hospital, Capital Medical University, No. 2, Chongwenmennei Street, Beijing, 100730, China
| | - He Zhao
- Beijing Institute of Technology, No. 5, Zhong Guan Cun South Street, Beijing, 100081, China.
| | - Hanruo Liu
- Beijing Institute of Technology, No. 5, Zhong Guan Cun South Street, Beijing, 100081, China; Beijing Tongren Hospital, Capital Medical University, No. 2, Chongwenmennei Street, Beijing, 100730, China
| | - Ningli Wang
- Beijing Tongren Hospital, Capital Medical University, No. 2, Chongwenmennei Street, Beijing, 100730, China
| | - Huiqi Li
- Beijing Institute of Technology, No. 5, Zhong Guan Cun South Street, Beijing, 100081, China.
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Zhang J, Zou H. Artificial intelligence technology for myopia challenges: A review. Front Cell Dev Biol 2023; 11:1124005. [PMID: 36733459 PMCID: PMC9887165 DOI: 10.3389/fcell.2023.1124005] [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: 12/14/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.
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Affiliation(s)
- Juzhao Zhang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haidong Zou
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,Shanghai Eye Diseases Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China,National Clinical Research Center for Eye Diseases, Shanghai, China,Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China,*Correspondence: Haidong Zou,
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Wan C, Fang J, Hua X, Chen L, Zhang S, Yang W. Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition. Front Comput Neurosci 2023; 17:1169464. [PMID: 37152298 PMCID: PMC10157024 DOI: 10.3389/fncom.2023.1169464] [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: 02/19/2023] [Accepted: 04/06/2023] [Indexed: 05/09/2023] Open
Abstract
Purpose To propose a five-category model for the automatic detection of myopic macular lesions to help grassroots medical institutions conduct preliminary screening of myopic macular lesions from limited number of color fundus images. Methods First, 1,750 fundus images of non-myopic retinal lesions and four categories of pathological myopic maculopathy were collected, graded, and labeled. Subsequently, three five-classification models based on Vision Outlooker for Visual Recognition (VOLO), EfficientNetV2, and ResNet50 for detecting myopic maculopathy were trained with data-augmented images, and the diagnostic results of the different trained models were compared and analyzed. The main evaluation metrics were sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), area under the curve (AUC), kappa and accuracy, and receiver operating characteristic curve (ROC). Results The diagnostic accuracy of the VOLO-D2 model was 96.60% with a kappa value of 95.60%. All indicators used for the diagnosis of myopia-free macular degeneration were 100%. The sensitivity, NPV, specificity, and PPV for diagnosis of leopard fundus were 96.43, 98.33, 100, and 100%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of diffuse chorioretinal atrophy were 96.88, 98.59, 93.94, and 99.29%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of patchy chorioretinal atrophy were 92.31, 99.26, 97.30, and 97.81%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of macular atrophy were 100, 98.10, 84.21, and 100%, respectively. Conclusion The VOLO-D2 model accurately identified myopia-free macular lesions and four pathological myopia-related macular lesions with high sensitivity and specificity. It can be used in screening pathological myopic macular lesions and can help ophthalmologists and primary medical institution providers complete the initial screening diagnosis of patients.
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Affiliation(s)
- Cheng Wan
- College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Jiyi Fang
- College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Xiao Hua
- Nanjing Star-mile Technology Co., Ltd., Nanjing, China
| | - Lu Chen
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
- Shenzhen Eye Institute, Shenzhen, China
| | - Shaochong Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
- Shenzhen Eye Institute, Shenzhen, China
- Shaochong Zhang,
| | - Weihua Yang
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
- Shenzhen Eye Institute, Shenzhen, China
- *Correspondence: Weihua Yang,
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Wang R, He J, Chen Q, Ye L, Sun D, Yin L, Zhou H, Zhao L, Zhu J, Zou H, Tan Q, Huang D, Liang B, He L, Wang W, Fan Y, Xu X. Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs. Ophthalmol Ther 2022; 12:469-484. [PMID: 36495394 PMCID: PMC9735275 DOI: 10.1007/s40123-022-00621-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/23/2022] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION The maculopathy in highly myopic eyes is complex. Its clinical diagnosis is a huge workload and subjective. To simply and quickly classify pathologic myopia (PM), a deep learning algorithm was developed and assessed to screen myopic maculopathy lesions based on color fundus photographs. METHODS This study included 10,347 ocular fundus photographs from 7606 participants. Of these photographs, 8210 were used for training and validation, and 2137 for external testing. A deep learning algorithm was trained, validated, and externally tested to screen myopic maculopathy which was classified into four categories: normal or mild tessellated fundus, severe tessellated fundus, early-stage PM, and advanced-stage PM. The area under the precision-recall curve, the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Cohen's kappa were calculated and compared with those of retina specialists. RESULTS In the validation data set, the model detected normal or mild tessellated fundus, severe tessellated fundus, early-stage PM, and advanced-stage PM with AUCs of 0.98, 0.95, 0.99, and 1.00, respectively; while in the external-testing data set of 2137 photographs, the model had AUCs of 0.99, 0.96, 0.98, and 1.00, respectively. CONCLUSIONS We developed a deep learning model for detection and classification of myopic maculopathy based on fundus photographs. Our model achieved high sensitivities, specificities, and reliable Cohen's kappa, compared with those of attending ophthalmologists.
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Affiliation(s)
- Ruonan Wang
- grid.452752.30000 0004 8501 948XDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, 200040 China ,grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China
| | - Jiangnan He
- grid.452752.30000 0004 8501 948XDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, 200040 China ,grid.24516.340000000123704535School of Medicine, Tongji University, Shanghai, China
| | - Qiuying Chen
- grid.452752.30000 0004 8501 948XDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, 200040 China ,grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China
| | - Luyao Ye
- grid.452752.30000 0004 8501 948XDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, 200040 China ,grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China
| | - Dandan Sun
- grid.452752.30000 0004 8501 948XDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, 200040 China ,grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China
| | - Lili Yin
- grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China
| | - Hao Zhou
- grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China
| | - Lijun Zhao
- Suzhou Life Intelligence Industry Research Institute, Suzhou, 215124 China
| | - Jianfeng Zhu
- grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China
| | - Haidong Zou
- grid.452752.30000 0004 8501 948XDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, 200040 China ,grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China
| | - Qichao Tan
- Suzhou Life Intelligence Industry Research Institute, Suzhou, 215124 China
| | - Difeng Huang
- Suzhou Life Intelligence Industry Research Institute, Suzhou, 215124 China
| | - Bo Liang
- grid.459411.c0000 0004 1761 0825School of Biology and Food Engineering, Changshu Institute of Technology, Changshu, China
| | - Lin He
- Suzhou Life Intelligence Industry Research Institute, Suzhou, 215124 China
| | - Weijun Wang
- grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China ,No. 100 Haining Road, Shanghai, 200080 China
| | - Ying Fan
- grid.452752.30000 0004 8501 948XDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, 200040 China ,grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China ,No. 380 Kangding Road, Shanghai, 200080 China
| | - Xun Xu
- grid.452752.30000 0004 8501 948XDepartment of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, 200040 China ,grid.16821.3c0000 0004 0368 8293Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628National Clinical Research Center for Eye Diseases, Shanghai, 200080 China ,grid.16821.3c0000 0004 0368 8293Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080 China ,Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080 China ,grid.412478.c0000 0004 1760 4628Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080 China
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22
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Yang J, Wu S, Zhang C, Yu W, Dai R, Chen Y. Global trends and frontiers of research on pathologic myopia since the millennium: A bibliometric analysis. Front Public Health 2022; 10:1047787. [PMID: 36561853 PMCID: PMC9763585 DOI: 10.3389/fpubh.2022.1047787] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 11/10/2022] [Indexed: 12/12/2022] Open
Abstract
Background and purpose Pathologic myopia (PM) is an international public health issue. This study aimed to analyze PM research trends by reporting on publication trends since 2000 and identifying influential journals, countries, authors, and keywords involved in PM. Methods A bibliometric analysis was performed to evaluate global production and development trends in PM since 2000 and the keywords associated with PM. Results A total of 1,435 publications were retrieved. PM has become a fascinating topic (with relative research interest ranging from 0.0018% in 2000 to 0.0044% in 2021) and a global public health issue. The top three countries with the highest number of publications were China, the USA, and Japan. The journals, authors, and institutions that published the most relevant literature came from these three countries. China exhibited the most rapid increase in the number of publications (from 0 in 2000 to 69 in 2021). Retina published the most papers on PM. Kyoko Ohno-Matsui and Tokyo Medical and Dental University contributed the most publications among authors and institutions, respectively. Based on keyword analysis, previous research emphasized myopic choroidal neovascularization and treatment, while recent hotspots include PM changes based on multimodal imaging, treatment, and pathogenesis. Keyword analysis also revealed that deep learning was the latest hotspot and has been used for the detection of PM. Conclusion Our results can help researchers understand the current status and future trends of PM. China, the USA, and Japan have the greatest influence, based on the number of publications, top journals, authors, and institutions. Current research on PM highlights the pathogenesis and application of novel technologies, including multimodal imaging and artificial intelligence.
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Affiliation(s)
- Jingyuan Yang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Shan Wu
- Department of Anaesthesiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Chenxi Zhang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Weihong Yu
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Rongping Dai
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Youxin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China,Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China,*Correspondence: Youxin Chen
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23
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Benvenuto GA, Colnago M, Dias MA, Negri RG, Silva EA, Casaca W. A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration. Bioengineering (Basel) 2022; 9:bioengineering9080369. [PMID: 36004894 PMCID: PMC9404907 DOI: 10.3390/bioengineering9080369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/31/2022] [Accepted: 08/03/2022] [Indexed: 11/26/2022] Open
Abstract
In ophthalmology, the registration problem consists of finding a geometric transformation that aligns a pair of images, supporting eye-care specialists who need to record and compare images of the same patient. Considering the registration methods for handling eye fundus images, the literature offers only a limited number of proposals based on deep learning (DL), whose implementations use the supervised learning paradigm to train a model. Additionally, ensuring high-quality registrations while still being flexible enough to tackle a broad range of fundus images is another drawback faced by most existing methods in the literature. Therefore, in this paper, we address the above-mentioned issues by introducing a new DL-based framework for eye fundus registration. Our methodology combines a U-shaped fully convolutional neural network with a spatial transformation learning scheme, where a reference-free similarity metric allows the registration without assuming any pre-annotated or artificially created data. Once trained, the model is able to accurately align pairs of images captured under several conditions, which include the presence of anatomical differences and low-quality photographs. Compared to other registration methods, our approach achieves better registration outcomes by just passing as input the desired pair of fundus images.
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Affiliation(s)
- Giovana A. Benvenuto
- Faculty of Science and Technology (FCT), São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil
| | - Marilaine Colnago
- Institute of Mathematics and Computer Science (ICMC), São Paulo University (USP), São Carlos 13566-590, Brazil
| | - Maurício A. Dias
- Faculty of Science and Technology (FCT), São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil
| | - Rogério G. Negri
- Science and Technology Institute (ICT), São Paulo State University (UNESP), São José dos Campos 12224-300, Brazil
| | - Erivaldo A. Silva
- Faculty of Science and Technology (FCT), São Paulo State University (UNESP), Presidente Prudente 19060-900, Brazil
| | - Wallace Casaca
- Institute of Biosciences, Letters and Exact Sciences (IBILCE), São Paulo State University (UNESP), São José do Rio Preto 15054-000, Brazil
- Correspondence:
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24
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Li F, Pan J, Yang D, Wu J, Ou Y, Li H, Huang J, Xie H, Ou D, Wu X, Wu B, Sun Q, Fang H, Yang Y, Xu Y, Luo Y, Zhang X. A Multicenter Clinical Study of the Automated Fundus Screening Algorithm. Transl Vis Sci Technol 2022; 11:22. [PMID: 35881410 PMCID: PMC9339691 DOI: 10.1167/tvst.11.7.22] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/16/2022] [Indexed: 12/25/2022] Open
Abstract
Purpose To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively. Methods We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.
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Affiliation(s)
- Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Jianying Pan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Dalu Yang
- Intelligent Healthcare Unit, Baidu, Beijing, China
| | | | - Yiling Ou
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Huiting Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Jiamin Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Huirui Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Dongmei Ou
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Xiaoyi Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Binghong Wu
- Intelligent Healthcare Unit, Baidu, Beijing, China
| | - Qinpei Sun
- Intelligent Healthcare Unit, Baidu, Beijing, China
| | - Huihui Fang
- Intelligent Healthcare Unit, Baidu, Beijing, China
| | - Yehui Yang
- Intelligent Healthcare Unit, Baidu, Beijing, China
| | - Yanwu Xu
- Intelligent Healthcare Unit, Baidu, Beijing, China
| | - Yan Luo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
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25
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Du R, Ohno-Matsui K. Novel Uses and Challenges of Artificial Intelligence in Diagnosing and Managing Eyes with High Myopia and Pathologic Myopia. Diagnostics (Basel) 2022; 12:diagnostics12051210. [PMID: 35626365 PMCID: PMC9141019 DOI: 10.3390/diagnostics12051210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 02/04/2023] Open
Abstract
Myopia is a global health issue, and the prevalence of high myopia has increased significantly in the past five to six decades. The high incidence of myopia and its vision-threatening course emphasize the need for automated methods to screen for high myopia and its serious form, named pathologic myopia (PM). Artificial intelligence (AI)-based applications have been extensively applied in medicine, and these applications have focused on analyzing ophthalmic images to diagnose the disease and to determine prognosis from these images. However, unlike diseases that mainly show pathologic changes in the fundus, high myopia and PM generate even more data because both the ophthalmic information and morphological changes in the retina and choroid need to be analyzed. In this review, we present how AI techniques have been used to diagnose and manage high myopia, PM, and other ocular diseases and discuss the current capacity of AI in assisting in preventing high myopia.
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26
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Li J, Wang L, Gao Y, Liang Q, Chen L, Sun X, Yang H, Zhao Z, Meng L, Xue S, Du Q, Zhang Z, Lv C, Xu H, Guo Z, Xie G, Xie L. Automated detection of myopic maculopathy from color fundus photographs using deep convolutional neural networks. EYE AND VISION (LONDON, ENGLAND) 2022; 9:13. [PMID: 35361278 PMCID: PMC8973805 DOI: 10.1186/s40662-022-00285-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 03/09/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Myopic maculopathy (MM) has become a major cause of visual impairment and blindness worldwide, especially in East Asian countries. Deep learning approaches such as deep convolutional neural networks (DCNN) have been successfully applied to identify some common retinal diseases and show great potential for the intelligent analysis of MM. This study aimed to build a reliable approach for automated detection of MM from retinal fundus images using DCNN models. METHODS A dual-stream DCNN (DCNN-DS) model that perceives features from both original images and corresponding processed images by color histogram distribution optimization method was designed for classification of no MM, tessellated fundus (TF), and pathologic myopia (PM). A total of 36,515 gradable images from four hospitals were used for DCNN model development, and 14,986 gradable images from the other two hospitals for external testing. We also compared the performance of the DCNN-DS model and four ophthalmologists on 3000 randomly sampled fundus images. RESULTS The DCNN-DS model achieved sensitivities of 93.3% and 91.0%, specificities of 99.6% and 98.7%, areas under the receiver operating characteristic curves (AUC) of 0.998 and 0.994 for detecting PM, whereas sensitivities of 98.8% and 92.8%, specificities of 95.6% and 94.1%, AUCs of 0.986 and 0.970 for detecting TF in two external testing datasets. In the sampled testing dataset, the sensitivities of four ophthalmologists ranged from 88.3% to 95.8% and 81.1% to 89.1%, and the specificities ranged from 95.9% to 99.2% and 77.8% to 97.3% for detecting PM and TF, respectively. Meanwhile, the DCNN-DS model achieved sensitivities of 90.8% and 97.9% and specificities of 99.1% and 94.0% for detecting PM and TF, respectively. CONCLUSIONS The proposed DCNN-DS approach demonstrated reliable performance with high sensitivity, specificity, and AUC to classify different MM levels on fundus photographs sourced from clinics. It can help identify MM automatically among the large myopic groups and show great potential for real-life applications.
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Affiliation(s)
- Jun Li
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China.,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China
| | - Lilong Wang
- Ping An Healthcare Technology, 9F Building B, PingAn IFC, No. 1-3 Xinyuan South Road, Beijing, 100027, China
| | - Yan Gao
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China.,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China
| | - Qianqian Liang
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China.,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China
| | - Lingzhi Chen
- Ping An Healthcare Technology, 9F Building B, PingAn IFC, No. 1-3 Xinyuan South Road, Beijing, 100027, China
| | - Xiaolei Sun
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China.,Shandong Eye Hospital of Shandong First Medical University, Jinan, 250021, China
| | | | | | - Lina Meng
- Qilu Hospital of Shandong University (Qingdao), Qingdao, 266035, China
| | - Shuyue Xue
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China.,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China
| | - Qing Du
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China.,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China
| | - Zhichun Zhang
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China.,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China
| | - Chuanfeng Lv
- Ping An Healthcare Technology, 9F Building B, PingAn IFC, No. 1-3 Xinyuan South Road, Beijing, 100027, China
| | - Haifeng Xu
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China.,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China
| | - Zhen Guo
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China.,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China
| | - Guotong Xie
- Ping An Healthcare Technology, 9F Building B, PingAn IFC, No. 1-3 Xinyuan South Road, Beijing, 100027, China. .,Ping An Healthcare and Technology Company Limited, Shanghai, 200030, China. .,Ping An International Smart City Technology Company Limited, Shenzhen, 518000, China.
| | - Lixin Xie
- Qingdao Eye Hospital of Shandong First Medical University, 5 Yanerdao Road, Qingdao, 266071, China. .,State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao, 266071, China.
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27
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Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia. Diagnostics (Basel) 2022; 12:diagnostics12030742. [PMID: 35328292 PMCID: PMC8947335 DOI: 10.3390/diagnostics12030742] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/10/2022] [Accepted: 03/16/2022] [Indexed: 12/20/2022] Open
Abstract
Pathologic myopia causes vision impairment and blindness, and therefore, necessitates a prompt diagnosis. However, there is no standardized definition of pathologic myopia, and its interpretation by 3D optical coherence tomography images is subjective, requiring considerable time and money. Therefore, there is a need for a diagnostic tool that can automatically and quickly diagnose pathologic myopia in patients. This study aimed to develop an algorithm that uses 3D optical coherence tomography volumetric images (C-scan) to automatically diagnose patients with pathologic myopia. The study was conducted using 367 eyes of patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary’s Hospital and Seoul St. Mary’s Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. The model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). Grad-CAM was used to visualize features affecting the detection of pathologic myopia. The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC) in identifying pathologic myopia.
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Li Y, Foo LL, Wong CW, Li J, Hoang QV, Schmetterer L, Ting DSW, Ang M. Pathologic myopia: advances in imaging and the potential role of artificial intelligence. Br J Ophthalmol 2022; 107:600-606. [PMID: 35288438 DOI: 10.1136/bjophthalmol-2021-320926] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 02/17/2022] [Indexed: 11/04/2022]
Abstract
Pathologic myopia is a severe form of myopia that can lead to permanent visual impairment. The recent global increase in the prevalence of myopia has been projected to lead to a higher incidence of pathologic myopia in the future. Thus, imaging myopic eyes to detect early pathological changes, or predict myopia progression to allow for early intervention, has become a key priority. Recent advances in optical coherence tomography (OCT) have contributed to the new grading system for myopic maculopathy and myopic traction maculopathy, which may improve phenotyping and thus, clinical management. Widefield fundus and OCT imaging has improved the detection of posterior staphyloma. Non-invasive OCT angiography has enabled depth-resolved imaging for myopic choroidal neovascularisation. Artificial intelligence (AI) has shown great performance in detecting pathologic myopia and the identification of myopia-associated complications. These advances in imaging with adjunctive AI analysis may lead to improvements in monitoring disease progression or guiding treatments. In this review, we provide an update on the classification of pathologic myopia, how imaging has improved clinical evaluation and management of myopia-associated complications, and the recent development of AI algorithms to aid the detection and classification of pathologic myopia.
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Affiliation(s)
- Yong Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Li-Lian Foo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Chee Wai Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Jonathan Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Quan V Hoang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Ophthalmology, Columbia University, New York City, New York, USA
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore.,School of Chemical and Biological Engineering, Nanyang Technological University, Singapore.,Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria.,Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore .,Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
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29
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Zhang C, Zhao J, Zhu Z, Li Y, Li K, Wang Y, Zheng Y. Applications of Artificial Intelligence in Myopia: Current and Future Directions. Front Med (Lausanne) 2022; 9:840498. [PMID: 35360739 PMCID: PMC8962670 DOI: 10.3389/fmed.2022.840498] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 12/17/2022] Open
Abstract
With the continuous development of computer technology, big data acquisition and imaging methods, the application of artificial intelligence (AI) in medical fields is expanding. The use of machine learning and deep learning in the diagnosis and treatment of ophthalmic diseases is becoming more widespread. As one of the main causes of visual impairment, myopia has a high global prevalence. Early screening or diagnosis of myopia, combined with other effective therapeutic interventions, is very important to maintain a patient's visual function and quality of life. Through the training of fundus photography, optical coherence tomography, and slit lamp images and through platforms provided by telemedicine, AI shows great application potential in the detection, diagnosis, progression prediction and treatment of myopia. In addition, AI models and wearable devices based on other forms of data also perform well in the behavioral intervention of myopia patients. Admittedly, there are still some challenges in the practical application of AI in myopia, such as the standardization of datasets; acceptance attitudes of users; and ethical, legal and regulatory issues. This paper reviews the clinical application status, potential challenges and future directions of AI in myopia and proposes that the establishment of an AI-integrated telemedicine platform will be a new direction for myopia management in the post-COVID-19 period.
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30
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Lu L, Ren P, Tang X, Yang M, Yuan M, Yu W, Huang J, Zhou E, Lu L, He Q, Zhu M, Ke G, Han W. AI-Model for Identifying Pathologic Myopia Based on Deep Learning Algorithms of Myopic Maculopathy Classification and "Plus" Lesion Detection in Fundus Images. Front Cell Dev Biol 2021; 9:719262. [PMID: 34722502 PMCID: PMC8554089 DOI: 10.3389/fcell.2021.719262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/20/2021] [Indexed: 01/24/2023] Open
Abstract
Background: Pathologic myopia (PM) associated with myopic maculopathy (MM) and “Plus” lesions is a major cause of irreversible visual impairment worldwide. Therefore, we aimed to develop a series of deep learning algorithms and artificial intelligence (AI)–models for automatic PM identification, MM classification, and “Plus” lesion detection based on retinal fundus images. Materials and Methods: Consecutive 37,659 retinal fundus images from 32,419 patients were collected. After excluding 5,649 ungradable images, a total dataset of 32,010 color retinal fundus images was manually graded for training and cross-validation according to the META-PM classification. We also retrospectively recruited 1,000 images from 732 patients from the three other hospitals in Zhejiang Province, serving as the external validation dataset. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and quadratic-weighted kappa score were calculated to evaluate the classification algorithms. The precision, recall, and F1-score were calculated to evaluate the object detection algorithms. The performance of all the algorithms was compared with the experts’ performance. To better understand the algorithms and clarify the direction of optimization, misclassification and visualization heatmap analyses were performed. Results: In five-fold cross-validation, algorithm I achieved robust performance, with accuracy = 97.36% (95% CI: 0.9697, 0.9775), AUC = 0.995 (95% CI: 0.9933, 0.9967), sensitivity = 93.92% (95% CI: 0.9333, 0.9451), and specificity = 98.19% (95% CI: 0.9787, 0.9852). The macro-AUC, accuracy, and quadratic-weighted kappa were 0.979, 96.74% (95% CI: 0.963, 0.9718), and 0.988 (95% CI: 0.986, 0.990) for algorithm II. Algorithm III achieved an accuracy of 0.9703 to 0.9941 for classifying the “Plus” lesions and an F1-score of 0.6855 to 0.8890 for detecting and localizing lesions. The performance metrics in external validation dataset were comparable to those of the experts and were slightly inferior to those of cross-validation. Conclusion: Our algorithms and AI-models were confirmed to achieve robust performance in real-world conditions. The application of our algorithms and AI-models has promise for facilitating clinical diagnosis and healthcare screening for PM on a large scale.
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Affiliation(s)
- Li Lu
- Department of Ophthalmology, Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Peifang Ren
- Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xuyuan Tang
- Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ming Yang
- Department of Ophthalmology, Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Minjie Yuan
- Department of Ophthalmology, Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wangshu Yu
- Department of Ophthalmology, Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jiani Huang
- Department of Ophthalmology, Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Enliang Zhou
- Department of Ophthalmology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Lixian Lu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Qin He
- Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Miaomiao Zhu
- Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Genjie Ke
- Department of Ophthalmology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Wei Han
- Department of Ophthalmology, Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images. Commun Biol 2021; 4:1225. [PMID: 34702997 PMCID: PMC8548495 DOI: 10.1038/s42003-021-02758-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 10/06/2021] [Indexed: 12/31/2022] Open
Abstract
Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale. Lu et al. develop a deep-learning based detection system for identifying pathological myopia and myopic macular legions in retinal fundus images. The system performance is comparable to human experts, but much faster, easing the burden of human time on screening for myopia.
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