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Ng Yin Ling C, Zhu X, Ang M. Artificial intelligence in myopia in children: current trends and future directions. Curr Opin Ophthalmol 2024; 35:463-471. [PMID: 39259652 DOI: 10.1097/icu.0000000000001086] [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: 09/13/2024]
Abstract
PURPOSE OF REVIEW Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management. RECENT FINDINGS The bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment. SUMMARY Artificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.
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Affiliation(s)
| | - Xiangjia Zhu
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University
- NHC Key Laboratory of Myopia and Related Eye Diseases; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences
- Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, China
| | - Marcus Ang
- Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, Singapore
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Yang HWW, Liang CKL, Chou SC, Wang HH, Chiang HK. Development and evaluation of a deep neural network model for orthokeratology lens fitting. Ophthalmic Physiol Opt 2024; 44:1224-1236. [PMID: 38980216 DOI: 10.1111/opo.13360] [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: 01/14/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/10/2024]
Abstract
PURPOSE To optimise the precision and efficacy of orthokeratology, this investigation evaluated a deep neural network (DNN) model for lens fitting. The objective was to refine the standardisation of fitting procedures and curtail subjective evaluations, thereby augmenting patient safety in the context of increasing global myopia. METHODS A retrospective study of successful orthokeratology treatment was conducted on 266 patients, with 449 eyes being analysed. A DNN model with an 80%-20% training-validation split predicted lens parameters (curvature, power and diameter) using corneal topography and refractive indices. The model featured two hidden layers for precision. RESULTS The DNN model achieved mean absolute errors of 0.21 D for alignment curvature (AC), 0.19 D for target power (TP) and 0.02 mm for lens diameter (LD), with R2 values of 0.97, 0.95 and 0.91, respectively. Accuracy decreased for myopia of less than 1.00 D, astigmatism exceeding 2.00 D and corneal curvatures >45.00 D. Approximately, 2% of cases with unique physiological characteristics showed notable prediction variances. CONCLUSION While exhibiting high accuracy, the DNN model's limitations in specifying myopia, cylinder power and corneal curvature cases highlight the need for algorithmic refinement and clinical validation in orthokeratology practice.
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Affiliation(s)
- Hsiu-Wan Wendy Yang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | | | - Shih-Chi Chou
- Ophthalmology Clinics, EyePlus Group, Taipei, Taiwan
| | - Hsin-Hui Wang
- Ophthalmology Clinics, EyePlus Group, Taipei, Taiwan
| | - Huihua Kenny Chiang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Xu S, Li L, Han W, Zhu Y, Hu Y, Li Z, Ruan Z, Zhou Z, Zhuo Y, Fu M, Yang X. Association Between Myopia and Pupil Diameter in Preschoolers: Evidence from a Machine Learning Approach Based on a Real-World Large-Scale Dataset. Ophthalmol Ther 2024; 13:2009-2022. [PMID: 38822998 PMCID: PMC11178758 DOI: 10.1007/s40123-024-00972-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/14/2024] [Indexed: 06/03/2024] Open
Abstract
INTRODUCTION Previous studies have explored the connections between various ocular biological parameters with myopia. Our previous study also found that pupil data can predict the myopic progression during the interventions for myopia. However, studies exploring the association between pupil diameter and myopia in preschoolers with myopia were lacking. Hence this study was aimed to investigate the association between pupil diameter and myopia in preschoolers with myopia based on a real-world, large-scale dataset. METHODS Data containing 650,671 preschoolers were collected from a total of 1943 kindergartens in Shenzhen, China. Refraction and pupil parameters were collected. After data filtering, the occurrence of myopia and its association with age, gender, pupil diameter, and other variables, were analyzed. Random forest (RF) and eXtreme gradient boosting (XGBoost) were selected from seven machine learning algorithms to build the model. The mean decrease accuracy (MDA), mean decrease Gini (MDG), and gain feature importance (GFI) techniques were employed to quantify the importance of pupil diameter and other features. RESULTS After the assessments, 51,325 valid records with complete pupil data were included, and 3468 (6.76%) were identified as myopia based on the calculated cycloplegic refraction. Preschoolers with myopia presented reduced pupil diameter and greater variation (5.00 ± 0.99 mm) compared to non-myopic preschoolers (6.22 ± 0.67 mm). A nonlinear relationship was found according to the scatterplots between pupil diameter and refraction (R2 = 0.14). Especially preschoolers with myopia had reduced pupil diameter compared to emmetropic preschoolers, but hyperope did not experience additional pupil enlargement. After adjusting for other covariates, this relationship is still consistent (P < 0.001). XGBoost and RF algorithms presented the highest performance and validated the importance of pupil diameter in myopia. CONCLUSIONS Based on a real-world large-scale dataset, the current study illuminated that preschoolers with myopia had a reduced pupil diameter compared to emmetropic preschoolers with a nonlinear pattern. Machine learning algorithms visualized and validated the pivotal role of pupil diameter in myopia. TRIAL REGISTRATION chictr.org Identifier: ChiCTR2200057391.
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Affiliation(s)
- Shengsong Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, 54 Xianlie South Road, Yuexiu District, Guangzhou, China
| | - Linling Li
- Department of Ophthalmology, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, 3012 Fuqiang Road, Futian District, Shenzhen, China
| | - Wenjing Han
- Department of Medical Imaging Technology, Yanjing Medical College, Capital Medical University, Beijing, China
| | - Yingting Zhu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, 54 Xianlie South Road, Yuexiu District, Guangzhou, China
| | - Yin Hu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, 54 Xianlie South Road, Yuexiu District, Guangzhou, China
| | - Zhidong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, 54 Xianlie South Road, Yuexiu District, Guangzhou, China
| | - Zhenbang Ruan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, 54 Xianlie South Road, Yuexiu District, Guangzhou, China
| | - Zhuandi Zhou
- Department of Ophthalmology, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, 3012 Fuqiang Road, Futian District, Shenzhen, China
| | - Yehong Zhuo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, 54 Xianlie South Road, Yuexiu District, Guangzhou, China
| | - Min Fu
- Department of Ophthalmology, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, 3012 Fuqiang Road, Futian District, Shenzhen, China.
| | - Xiao Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, 54 Xianlie South Road, Yuexiu District, Guangzhou, China.
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Tang W, Li J, Fu X, Lin Q, Zhang L, Luo X, Zhao W, Liao J, Xu X, Wang X, Zhang H, Li J. Machine learning-based nomogram to predict poor response to overnight orthokeratology in Chinese myopic children: A multicentre, retrospective study. Acta Ophthalmol 2024. [PMID: 38516719 DOI: 10.1111/aos.16678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 02/02/2024] [Accepted: 03/13/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE To develop and validate an effective nomogram for predicting poor response to orthokeratology. METHODS Myopic children (aged 8-15 years) treated with orthokeratology between February 2018 and January 2022 were screened in four hospitals of different tiers (i.e. municipal and provincial) in China. Potential predictors included 32 baseline clinical variables. Nomogram for the outcome (1-year axial elongation ≥0.20 mm: poor response; <0.20 mm: good response) was computed from a logistic regression model with the least absolute shrinkage and selection operator. The data from the First Affiliated Hospital of Chengdu Medical College were randomly assigned (7:3) to the training and validation cohorts. An external cohort from three independent multicentre was used for the model test. Model performance was assessed by discrimination (the area under curve, AUC), calibration (calibration plots) and utility (decision curve analysis). RESULTS Between January 2022 and March 2023, 1183 eligible subjects were screened from the First Affiliated Hospital of Chengdu Medical College, then randomly divided into training (n = 831) and validation (n = 352) cohorts. A total of 405 eligible subjects were screened in the external cohort. Predictors included in the nomogram were baseline age, spherical equivalent, axial length, pupil diameter, surface asymmetry index and parental myopia (p < 0.05). This nomogram demonstrated excellent calibration, clinical net benefit and discrimination, with the AUC of 0.871 (95% CI 0.847-0.894), 0.863 (0.826-0.901) and 0.817 (0.777-0.857) in the training, validation and external cohorts, respectively. An online calculator was generated for free access (http://39.96.75.172:8182/#/nomogram). CONCLUSION The nomogram provides accurate individual prediction of poor response to overnight orthokeratology in Chinese myopic children.
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Affiliation(s)
- Wenting Tang
- Department of Ophthalmology, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
| | - Jiaqian Li
- Department of Ophthalmology, The First People's Hospital of Ziyang, Ziyang, China
| | - Xuelin Fu
- Department of Ophthalmology, Chengdu First People's Hospital, Chengdu, China
| | - Quan Lin
- Department of Ophthalmology, Nanning Aier Eye Hospital, Nanning, China
| | - Li Zhang
- Department of Ophthalmology, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
| | - Xiangning Luo
- Department of Ophthalmology, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
| | - Wenjing Zhao
- Department of Ophthalmology, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
| | - Jia Liao
- Department of Ophthalmology, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
| | - Xinyue Xu
- Department of Ophthalmology, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
| | - Xiaoqin Wang
- Department of Ophthalmology, Chengdu First People's Hospital, Chengdu, China
| | - Huidan Zhang
- Department of Ophthalmology, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
| | - Jing Li
- Department of Ophthalmology, The First Affiliated Hospital of Chengdu Medical College, Chengdu Medical College, Chengdu, China
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Galdo B, Pazos C, Pardo J, Solar A, Llamas D, Fernández-Blanco E, Pazos A. Artificial intelligence in paediatrics: Current events and challenges. An Pediatr (Barc) 2024; 100:195-201. [PMID: 38461129 DOI: 10.1016/j.anpede.2024.02.009] [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: 01/08/2024] [Accepted: 02/05/2024] [Indexed: 03/11/2024] Open
Abstract
This article examines the use of artificial intelligence (AI) in the field of paediatric care within the framework of the 7P medicine model (Predictive, Preventive, Personalized, Precise, Participatory, Peripheral and Polyprofessional). It highlights various applications of AI in the diagnosis, treatment and management of paediatric diseases as well as the role of AI in prevention and in the efficient management of health care resources and the resulting impact on the sustainability of public health systems. Successful cases of the application of AI in the paediatric care setting are presented, placing emphasis on the need to move towards a 7P health care model. Artificial intelligence is revolutionizing society at large and has a great potential for significantly improving paediatric care.
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Affiliation(s)
- Brais Galdo
- Universidad de A Coruña, A Coruña, Spain; INIBIC, A Coruña, Spain; RNASA-IMEDIR, A Coruña, Spain; Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain; Avances en Telemedicina e Informática Sanitaria, A Coruña, Spain
| | - Carla Pazos
- New Vision University, Faculty of Medicine, Tiflis, Georgia
| | - Jerónimo Pardo
- Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain
| | - Alfonso Solar
- Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain
| | - Daniel Llamas
- INIBIC, A Coruña, Spain; Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain; Avances en Telemedicina e Informática Sanitaria, A Coruña, Spain
| | - Enrique Fernández-Blanco
- Universidad de A Coruña, A Coruña, Spain; INIBIC, A Coruña, Spain; RNASA-IMEDIR, A Coruña, Spain; CITIC, A Coruña, Spain
| | - Alejandro Pazos
- Medical University of Byalistok, Byalistok, Podlaquia, Poland.
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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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Yu D, Wang L, Zhou X, Liu L, Wu S, Tang Q, Zhang X. Sleep Quality is Associated with Axial Length Elongation in Myopic Children Receiving Orthokeratology: A Retrospective Study. Nat Sci Sleep 2023; 15:993-1001. [PMID: 38050564 PMCID: PMC10693766 DOI: 10.2147/nss.s421407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/14/2023] [Indexed: 12/06/2023] Open
Abstract
Purpose To identify potential demographic and lifestyle factors associated with progression of myopia with orthokeratology (ortho-k) treatment via follow-up of axial length (AL). Methods In this retrospective observational study, demographics, ocular parameters, near-work distance, outdoor activities, and sleep quality were analyzed in 134 children with myopia aged 8~15 years using ortho-k and a follow-up for one year. Results Compared with the slow progression group, the participants in the fast progression group were younger in age (10.55 ±1.70 years vs 9.90 ±1.18 years, P = 0.009), demonstrated higher spherical equivalent (SE) value (-2.52 ±0.63 diopters (D) vs -3.05 ±0.89 D, P < 0.001), shorter near-work distance (P = 0.010), and poorer sleep quality (Pittsburgh sleep quality index [PSQI], 4.79 ±1.29 vs 3.81 ±1.38, P < 0.001) in the one-year follow-up. Furthermore, multivariate linear regression analyses showed that baseline age (B =-0.020, P = 0.020), SE (B = 0.0517, P < 0.001), and total PSQI (B=0.026, P = 0.001) were associated with axial elongation. Advanced logistic regression analyses demonstrated that shorter average near-work distance (P = 0.034), higher SE value (P = 0.023), and poorer sleep quality (P = 0.003) were associated with fast axial elongation. Conclusion Sleep quality is one of the key factors associated with axial elongation in children with myopia after using ortho-k for one year. Further studies are required to confirm this observation and expand its practical applications.
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Affiliation(s)
- Dongyi Yu
- Department of Ophthalmology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, People’s Republic of China
- Department of Ophthalmology, Kunshan First People’s Hospital Affiliated to Jiangsu University, Suzhou, Jiangsu, 215300, People’s Republic of China
| | - Libo Wang
- Department of Ophthalmology, Kunshan First People’s Hospital Affiliated to Jiangsu University, Suzhou, Jiangsu, 215300, People’s Republic of China
| | - Xin Zhou
- Department of Ophthalmology, Kunshan First People’s Hospital Affiliated to Jiangsu University, Suzhou, Jiangsu, 215300, People’s Republic of China
| | - Lili Liu
- Department of Ophthalmology, Kunshan First People’s Hospital Affiliated to Jiangsu University, Suzhou, Jiangsu, 215300, People’s Republic of China
| | - Shuang Wu
- Department of Ophthalmology, Kunshan First People’s Hospital Affiliated to Jiangsu University, Suzhou, Jiangsu, 215300, People’s Republic of China
| | - Qing Tang
- Department of Neurology, Kunshan First People’s Hospital Affiliated to Jiangsu University, Suzhou, Jiangsu, 215300, People’s Republic of China
| | - Xiaofeng Zhang
- Department of Ophthalmology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, People’s Republic of China
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Du HQ, Dai Q, Zhang ZH, Wang CC, Zhai J, Yang WH, Zhu TP. Artificial intelligence-aided diagnosis and treatment in the field of optometry. Int J Ophthalmol 2023; 16:1406-1416. [PMID: 37724269 PMCID: PMC10475639 DOI: 10.18240/ijo.2023.09.06] [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: 04/11/2023] [Accepted: 06/14/2023] [Indexed: 09/20/2023] Open
Abstract
With the rapid development of computer technology, the application of artificial intelligence (AI) to ophthalmology has gained prominence in modern medicine. As modern optometry is closely related to ophthalmology, AI research on optometry has also increased. This review summarizes current AI research and technologies used for diagnosis in optometry, related to myopia, strabismus, amblyopia, optical glasses, contact lenses, and other aspects. The aim is to identify mature AI models that are suitable for research on optometry and potential algorithms that may be used in future clinical practice.
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Affiliation(s)
- Hua-Qing Du
- Zhejiang University, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310027, Zhejiang Province, China
| | - Qi Dai
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
| | - Zu-Hui Zhang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
| | - Chen-Chen Wang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
| | - Jing Zhai
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
| | - Wei-Hua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Tie-Pei Zhu
- Eye Center, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310002, Zhejiang Province, China
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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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Wang S, Ji Y, Bai W, Ji Y, Li J, Yao Y, Zhang Z, Jiang Q, Li K. Advances in artificial intelligence models and algorithms in the field of optometry. Front Cell Dev Biol 2023; 11:1170068. [PMID: 37187617 PMCID: PMC10175695 DOI: 10.3389/fcell.2023.1170068] [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: 02/27/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.
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Affiliation(s)
- Suyu Wang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yuke Ji
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Wen Bai
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jiajun Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yujia Yao
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Ziran Zhang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Qin Jiang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
| | - Keran Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
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