Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization

Wu, Xiaohang; Liu, Lixue; Zhao, Lanqin; Guo, Chong; Li, Ruiyang; Wang, Ting; Yang, Xiaonan; Xie, Peichen; Liu, Yizhi; Lin, Haotian

doi:10.21037/atm-20-976

Cited by 28 publications

(41 citation statements)

References 71 publications

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“…They also provided a detailed summary of the pros and cons of this emerging technique for both computer scientists and ophthalmologists and specified the clinical and technical aspects to address deep learning challenges and future directions. Some studies [ 56 , 105 , 106 ] discussed the importance of clinical considerations and potential challenges for clinical adoption and telemedicine integration to reduce cost, increase accuracy, and facilitate health care accessibility. Ting et al [ 53 ] described the importance of deploying deep learning algorithms within clinical settings.…”

Section: Resultsmentioning

confidence: 99%

“…A major difficulty of each algorithm is its validity in multiple patient cohorts with diverse conditions. Therefore, for a DLM to be sturdy, it must be effective across various data sets [ 105 ].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, annotation variety is more important than the actual number of annotations [ 43 ]. Therefore, doctors must develop a public data set with high-resolution images that meet the quality assessment system [ 105 ]. Furthermore, it must be accessible and include multiethnicities to ensure generalization capability [ 108 ].…”

Section: Discussionmentioning

confidence: 99%

“…To date, only a few studies have experienced AI technologies in teleophthalmology [ 56 ]; photography using smartphones can be used as a diagnostic tool for ocular diseases [ 105 ]. Nowadays, there is a great need for remote disease monitoring and screening [ 107 ], especially during the COVID-19 pandemic and the vast infection transmission [ 150 ].…”

Section: Discussionmentioning

confidence: 99%

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Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Saeed¹,

Abdullah²,

Che-Hamzah³

et al. 2021

J Med Internet Res

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Background Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease using recently developed generative adversarial networks (GANs). Objective This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology. Methods To organize this review comprehensively, articles and reviews were collected using the following keywords: (“Glaucoma,” “optic disc,” “blood vessels”) and (“receptive field,” “loss function,” “GAN,” “Generative Adversarial Network,” “Deep learning,” “CNN,” “convolutional neural network” OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020. Results We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease. Conclusions Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Saeed¹,

Abdullah²,

Che-Hamzah³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…At present, AI technology is being used more widely for anterior segment diseases, such as keratoconus, infectious keratitis, refractive surgery, corneal transplantation, cataract, angle closure glaucoma, dry eye and pterygium (Wu et al, 2020;Ting et al, 2021). A computer-aided pterygium screening platform developed by Zaki et al has been used to classify pterygium and non-pterygium cases (Wan Zaki et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning

Jin

Zhu³

et al. 2021

Front. Psychol.

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Objective: This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs.Methods: A total of 1,220 anterior segment photographs of normal eyes and pterygium patients were collected for training (using 750 images) and testing (using 470 images) to develop an intelligent pterygium diagnostic model. The images were classified into three categories by the experts and the intelligent pterygium diagnosis system: (i) the normal group, (ii) the observation group of pterygium, and (iii) the operation group of pterygium. The intelligent diagnostic results were compared with those of the expert diagnosis. Indicators including accuracy, sensitivity, specificity, kappa value, the area under the receiver operating characteristic curve (AUC), as well as 95% confidence interval (CI) and F1-score were evaluated.Results: The accuracy rate of the intelligent diagnosis system on the 470 testing photographs was 94.68%; the diagnostic consistency was high; the kappa values of the three groups were all above 85%. Additionally, the AUC values approached 100% in group 1 and 95% in the other two groups. The best results generated from the proposed system for sensitivity, specificity, and F1-scores were 100, 99.64, and 99.74% in group 1; 90.06, 97.32, and 92.49% in group 2; and 92.73, 95.56, and 89.47% in group 3, respectively.Conclusion: The intelligent pterygium diagnosis system based on deep learning can not only judge the presence of pterygium but also classify the severity of pterygium. This study is expected to provide a new screening tool for pterygium and benefit patients from areas lacking medical resources.

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Automated cataract disease detection on anterior segment eye images using adaptive thresholding and fine tuned inception-v3 model

Faizal

Rajput

Tripathi

et al. 2023

Biomedical Signal Processing and Control

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Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization

Cited by 28 publications

References 71 publications

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning

Automated cataract disease detection on anterior segment eye images using adaptive thresholding and fine tuned inception-v3 model

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