Editorial
Volume 12 Issue 9 - 2020
Is Telemedicine the Future of Ophthalmology?
Professor of Ophthalmology, Yerevan State Medical University, Republic of Armenia
*Corresponding Author: Marianne L Shahsuvaryan, Professor of Ophthalmology, Yerevan State Medical University, Republic of Armenia.
Received: July 22, 2021; Published: August 28, 2021
The incidence and prevalence of chronic eye diseases such as glaucoma, age-related macular degeneration, diabetic retinopathy is increasing due to increased life expectancy in many parts of the world and medical advances that control the progression of the disease rather than treating and eliminating the causes. “We face enormous challenges in avoiding vision impairment as the global population grows and ages” [1]. The forecast for the year 2040 indicates that the number of patients with glaucoma will reach 111.8 million [2], with age-related macular degeneration - 288 million [3], with diabetic retinopathy - 224 million [4] respectively.
References
- GBD 2019 Blindness and Vision Impairment Collaborators; Vision Loss Expert Group of the Global Burden of Disease Study. Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study”. Lancet Glob Health2 (2021): e130-e143.
- Tham YC., et al. “Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis”. Ophthalmology11 (2014): 2081-2090.
- Pascolini D and Mariotti SP. “Global estimates of visual impairment: 2010”. British Journal of Ophthalmology5 (2012): 614-618.
- Strengthening diagnosis and treatment of diabetic retinopathy in the South-East Asia Region”. New Delhi: World Health Organization, Regional Office for South-East Asia (2020).
- Turing AM. “Computing machinery and intelligence”. Mind 236 (1950): 433-460.
- Mintz Y and Brodie R. “Introduction to artificial intelligence in medicine”. Minimally Invasive Therapy and Allied Technologies2 (2019): 73-81.
- Gargeya R and Leng T. “Automated identification of diabetic retinopathy using deep learning”. Ophthalmology7 (2017): 962-969.
- Abramoff MD., et al. “Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices”. NPJ Digital Medicine 1 (2018): 39.
- Van Der Heijden AA., et al. “Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the horn diabetes care system”. Acta Ophthalmologica1 (2018): 63-68.
- Burlina PM., et al. “Automated grading of age-related macular degeneration from color fundus images using deep convolutional Neural Networks”. JAMA: The Journal of the American Medical Association Ophthalmology11 (2017): 1170-1176.
- Mayro EL., et al. “The impact of artificial intelligence in the diagnosis and management of glaucoma”. Eye1 (2020): 1-11.
- De Fauw J., et al. “Clinically applicable deep learning for diagnosis and referral in retinal disease”. Nature Medicine9 (2018): 1342-1350.
- Korot E., et al. “Will AI replace ophthalmologists?” Translational Vision Science and Technology 2 (2020): 2.
- Di Blasi Z., et al. “Influence of context effects on health outcomes: a systematic review”. Lancet 357 (2001): 757-762.
Citation: Marianne L Shahsuvaryan. “Is Telemedicine the Future of Ophthalmology?”. EC Ophthalmology 12.9 (2021): 01-03.
Copyright: © 2021 Marianne L Shahsuvaryan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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