Applications of Artificial Intelligence in Myopia: Current and Future Directions

Zhang, Chenchen; Zhao, Jing; Zhu, Zhe; Li, Yanxia; Li, Ké; Wang, Yuanping; Zheng, Yajuan

doi:10.3389/fmed.2022.840498

Cited by 16 publications

(9 citation statements)

References 93 publications

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“…In particular, as a result of the outbreak of COVID-19 in 2019, the remote medical systems will be more important [ 33 , 34 ]. An AI-integrated telemedicine platform will be a new direction of myopia healthcare in the post-COVID-19 period [ 35 ]. In this study, we developed an effective deep learning model using EfficientNet-B8 based on 8210 color fundus photographs and demonstrated its potential in screening myopic maculopathy.…”

Section: Discussionmentioning

confidence: 99%

Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs

Wang

et al. 2022

Ophthalmol Ther

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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. Supplementary Information The online version contains supplementary material available at 10.1007/s40123-022-00621-9.

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Section: Discussionmentioning

confidence: 99%

Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs

Wang

et al. 2022

Ophthalmol Ther

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“…Even mild myopia increases the risk of eye problems (like retinal blur, accommodation problem). High myopia and pathological myopia (PM) significantly raise the risk of irreversible vision loss, including glaucoma, retinal detachment, myopic macular degeneration (MMD), and macular choroidal neovascularization [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…The ability of eye care professionals to plan effective interventions depends on the prompt identification of people at a high risk of acquiring myopia and the regular, repetitive monitoring of its development and associated difficulties [ 2 ]. The growing weight of myopia, however, might be too great for the current healthcare systems to handle [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Role of Machine Learning and Artificial Intelligence in the Diagnosis and Treatment of Refractive Errors for Enhanced Eye Care: A Systematic Review

Alnahedh,

Taha

2024

Cureus

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A significant contributor to blindness and visual impairment globally is uncorrected refractive error. To plan effective interventions, eye care professionals must promptly identify people at a high risk of acquiring myopia, and monitor disease progress. Artificial intelligence (AI) and machine learning (ML) have enormous potential to improve diagnosis and treatment. This systematic review explores the current state of ML and AI applications in the diagnoses and treatment of refractory errors in optometry. A systematic review and meta-analysis of studies evaluating the diagnostic performance of AI-based tools in PubMed was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To find relevant studies on the use of ML or AI in the diagnosis or treatment of refractive errors in optometry, a thorough search was conducted in various electronic databases such as PubMed, Google Scholar, and Web of Science. The search was limited to studies published between January 2015 and December 2022. The search terms used were "refractive errors," "myopia," "optometry," "machine learning," "ophthalmology," and "artificial intelligence." A total of nine studies met the inclusion criteria and were included in the final analysis. ML is increasingly being utilized for automating clinical data processing as AI technology progresses, making the formerly labor-intensive work possible. AI models that primarily use a neural network demonstrated exceptional efficiency and performance in the analysis of vast medical data, rivaling board-certified, healthcare professionals. Several studies showed that ML models could support diagnosis and clinical decision-making. Moreover, an ML algorithm predicted future refraction values in patients with myopia. AI and ML models have great potential to improve the diagnosis and treatment of refractive errors in optometry.

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“…Various works have been proposed to review the research of AI in myopia ( Foo et al, 2021 ; Du and Ohno-Matsui, 2022 ; Zhang et al, 2022 ). To our knowledge, none of the existing work has elaborated technical details of the discussed work, thus fail to make readers more aware of strengths and limitations of each AI method in specific tasks of myopia.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence technology for myopia challenges: A review

Zhang

Zou

2023

Front. Cell Dev. Biol.

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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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Applications of Artificial Intelligence in Myopia: Current and Future Directions

Cited by 16 publications

References 93 publications

Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs

Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs

Role of Machine Learning and Artificial Intelligence in the Diagnosis and Treatment of Refractive Errors for Enhanced Eye Care: A Systematic Review

Artificial intelligence technology for myopia challenges: A review

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