The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Nuzzi, Raffaele; Boscia, Giacomo; Ricardi, Federico

doi:10.3389/fmed.2021.710329

Cited by 33 publications

(15 citation statements)

References 103 publications

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“…Tools can match or even outperform physicians and can make access to screening broader and less expensive; algorithms and devices are already clinically available (IDx-DR by Digital Diagnostics, Coralville, IA, United States; SELENA+ by EyRIS, Singapore) and have been authorized for use in multiple fundus cameras (407)(408)(409)(410)(411)(412). Training models for AI has been steadily increasing for diagnosing DR from fundus pictures, with accuracy, sensitivity, and specificity improving over time (reaching 95.7, 97.5, and 98%, respectively) (412)(413)(414)(415)(416).…”

Section: Artificial Intelligence and Diabetic Retinopathymentioning

confidence: 99%

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

et al. 2022

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The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

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Section: Artificial Intelligence and Diabetic Retinopathymentioning

confidence: 99%

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

et al. 2022

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“…Методы машинного обучения находят все большее применение в медицине [11][12][13][14], в том числе и для диагностики и лечения глаукомы [15][16][17][18][19]. В нашей работе мы используем метод машинного обучения DD-SIMCA, разработанный двумя соавторами настоящей работы.…”

Section: Ophthalmology In Russia 2022;19(3):549-556 введениеunclassified

Machine Learning Methods in the Comparative Evaluation of Various Approaches to the Surgical Treatment of Primary Angle Closure

Kurysheva

Pomerantsev²,

Rodionova³

et al. 2022

Oftalʹmologiâ

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Purpose. To evaluate the application of the principal component analysis (PCA) and DD-SIMCA in a comparative analysis of the surgical treatment of primary angle closure.Material and methods. The prospective study included 90 patients. Group 1 — 30 patients with primary angle closure (PAC) with planned laser peripheral iridotomy (LPI). Group 2 — 30 patients with PAC, with planned phacoemulsification with intraocular lens implantation (PE+IOL). Group 3 — 30 eyes without ophthalmic pathology. All subjects underwent SS-OCT. Thirty-seven parameters were analyzed, including intraocular pressure, choroidal thickness in the macula, anterior chamber depth, lens vault, iris curvature and thickness, angle opening distance, and iridotrabecular space at 500 µm and 750 µm from the scleral spur. Since all these parameters correlate with each other, machine learning methods were used: PCA and the DD-SIMCA one-class classification method. For this purpose graphs of scores and loads in the PCA model for groups 1 and 2 were plotted. In the score plot, patients with PAC with average and extreme eye parameters were identified, and in the loading plot, relationships between the parameters of patients with PM were used to analyze correlations in the future. In the DD-SIMCA method, group 1 is taken as representatives of the target class.Results. A classification model based on 2 principal components with a given type I error α = 0.01 demonstrated a sensitivity of 100 % for patients in its own group and a sensitivity of 93 % for patients in group 2. These results confirm similarity of group 1 and group 2. The specificity for the control group was 100 %, and this group located far from the target group.Conclusion. Machine learning methods make it possible to compare groups with multivariate and correlated parameters. PCA allows the identification of patients with extreme parameters and the evaluation of correlations between multiple parameters. DDSIMCA confirms the validity of comparing the results of treatment with LPI and FE + IOL.

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“…Many machine learning techniques have been developed in ophthalmology, including applications for the identification of retinal landmarks, retinal pathology segmentation, and retinal disease classification. As a result, reviews of deep learning works have been actively published, with some covering specific domains and some covering ophthalmology in general [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. A review of the applications in sub-domains has the advantage of providing detailed and rich content, but it can be difficult to note the importance of the applications in a large context, and a review may contain information that is not meaningful from an engineer’s point of view.…”

Section: Introductionmentioning

confidence: 99%

Review of Machine Learning Applications Using Retinal Fundus Images

2022

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Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

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The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Cited by 33 publications

References 103 publications

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

Machine Learning Methods in the Comparative Evaluation of Various Approaches to the Surgical Treatment of Primary Angle Closure

Review of Machine Learning Applications Using Retinal Fundus Images

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