165 Glaucoma - Tips, consejos y experiencias Vol Abstract Number: 1489-A0173). ; 2019:Poster Number: A0173. 48. Kolomeyer A, Nayak N, Szirth B, et al. Fundus Autofluorescence Imaging in an Ocular Screening Program. In: International journal of telemedicine and applications.; 2012. 49. Khouri A, Szirth B, Shahid K, et al. Software-Assisted Optic Nerve Assessment for Glaucoma Tele-Screening. In: Vol 14. Telemedicine journal and e-health: the official journal of the American Telemedicine Association.; 2008:261-265. 50. Arcadu F, Benmansour F, Maunz A, et al. Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs. Invest Ophthalmol Vis Sci. 2019;60(4):852-857. 51. Devalla SK, Liang Z, Pham TH, et al. Glaucoma management in the era of artificial intelligence. Br J Ophthalmol. 2020;104(3):301-311. 52. Kapoor R, Whigham B, Al-Asward L. Artificial Intelligence and Optical Coherence Tomography Imaging. In: Vol 2019;8(2). Asia Pac Journal of Ophthalmology; :187-194. 53. Ooms A, Caterfino A, Prasad N, et al. Robotics and Artificial Intelligence in the Management of Vision Threatening Disease. Invest Ophthalmol Vis Sci. 2019;60(9):1486-1486. 54. Usher D, Dumskyj M, Himiga M, et al. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med. 2003;21(1):84-90. 55. Stead WW. Clinical Implications and Challenges of Artificial Intelligence and Deep Learning. JAMA. 2018;320(11):1107-1108. 56. Becevic M, Clarke MA, Alnijoumi MM, et al. Robotic Telepresence in a Medical Intensive Care Unit—Clinicians’ Perceptions. Perspect Health Inf Manag. 2015;12(Summer). 57. Broadbent E, Stafford R, MacDonald B. Acceptance of Healthcare Robots for the Older Population: Review and Future Directions. Int J Soc Robot. 2009;1(4):319. 58. Koceski S, Koceska N. Evaluation of an Assistive Telepresence Robot for Elderly Healthcare. J Med Syst. 2016;40(5):121. 59. Shaikh IS, Ooms A, Habiel M, et al. Robotic Telepresence in the Management of Vision Threatening Disease. Invest Ophthalmol Vis Sci. 2020;61(7):1600-1600. 60. Newman-Casey PA, Weizer JS, Heisler M, et al. Systematic Review of Educational Interventions to Improve Glaucoma Medication Adherence. Semin Ophthalmol. 2013;28(3):191 201. 61. Murphy J, Coster G. Issues in Patient Compliance. Drugs. 1997;54(6):797-800. 62. Jin J, Sklar GE, Min Sen Oh V, et al. Factors affecting therapeutic compliance: A review from the patient’s perspective. Ther Clin Risk Manag. 2008;4(1):269-286. 63. Ooms A, Shaikh IS, Patel N, et al. Use of Telepresence Robots in Glaucoma Patient Education. Invest Ophthalmol Vis Sci. 2020;61(7):3091-3091. 64. Diabetic Retinopathy- silently blinding millions of people world-wide • IAPB Vision Atlas. IAPB Vision Atlas. Accessed July 24, 2020. http://atlas.iapb.org/vision-trends/diabetic retinopathy/ 65. Mohammadpour M, Heidari Z, Mirghorbani M, et al. Smartphones, tele-ophthalmology, and VISION 2020. Int J Ophthalmol. 2017;10(12):1909-1918. 66. Shah K, Gandhi A, Natarajan S. Diabetic Retinopathy Awareness and Associations with Multiple Comorbidities: Insights from DIAMOND Study. Indian J Endocrinol Metab. 2018;22(1):30-35. 67. Khouri CA, Ooms A, Thangmathesvaran L, et al. Characteristics of type 1 diabetes patients using continuous glucose monitoring systems and development of retinopathy. 2019;60(9). 68. Hwang D-K, Hsu C-C, Chang K-J, et al. Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics. 2019;9(1):232-245. 69. Bhuiyan A, Wong TY, Ting DSW, et al. Artificial Intelligence to Stratify Severity of Age Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD. Transl Vis Sci Technol. 2020;9(2):25-25. 70. Andonegui J, Aliseda D, Serrano L, et al. Evaluation of a telemedicine model to follow up patients with exudative age-related macular degeneration. Retina Phila Pa. 2016;36(2):279-284. 71. Starr MR, Barkmeier AJ, Engman SJ,et al. Telemedicine in the Management of Exudative Age-Related Macular Degeneration within an Integrated health care System. Am J Ophthalmol. 2019;208:206-210. 72. Wang Y-Z, He Y-G, Mitzel G, et al. Handheld Shape Discrimination Hyperacuity Test on a Mobile Device for Remote Monitoring of Visual Function in Maculopathy. Invest Ophthalmol Vis Sci. 2013;54(8):5497-5505. 73. Bowman R, Foster A. Testing the red reflex. Community Eye Health. 2018;31(101):23. 74. Sanguansak T, Morley K, Morley M, et al. Comparing smartphone camera adapters in imaging post-operative cataract patients. J Telemed Telecare. 2017;23(1):36-43. 75. Rajalakshmi R, Subashini R, Anjana RM, et al. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye. 2018;32(6):1138-1144. 76. Rathi S, Tsui E, Mehta N, et al. The Current State of Teleophthalmology in the United States. Ophthalmology. 2017;124(12):1729-1734. 77. Rhodes LA, Huisingh CE, McGwin G, et al. Eye Care Quality and Accessibility Improvement in the Community (EQUALITY): impact of an eye health education program on patient knowledge about glaucoma and attitudes about eye care. Patient Relat Outcome Meas. 2016;7:37-48. 78. Jonas JB. Clinical implications of peripapillary atrophy in glaucoma. Curr Opin Ophthalmol. 2005;16(2):84-88. 79. Rockwood EJ, Anderson DR. Acquired peripapillary changes and progression in glaucoma. Graefes Arch Clin Exp Ophthalmol Albrecht Von Graefes Arch Klin Exp Ophthalmol. 1988;226(6):510-515.
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