Title |
Big Data in Neuro-Ophthalmology: International Classification of Diseases Codes |
Creator |
Leanne Stunkel, MD |
Affiliation |
John F. Hardesty, MD Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, Missouri; and Department of Neurology, Washington University in St. Louis, St. Louis, Missouri |
Abstract |
Advancements in technology, including the widespread adoption of electronic medical record keeping, expanding access to imaging techniques such as nonmydriatic fundus photography the expansion of telemedicine, and the application of artificial intelligence to the field of medicine, promise to revolutionize our ability to diagnose and treat neuro-ophthalmic disease. 'Big data,' for our purposes referring to the dramatic increase in the amount of medical data accessible to researchers, has the potential to accelerate investigations into diagnosis and treatment of neuro-ophthalmic conditions. Meanwhile, machine learning may provide a feasible avenue for analyzing an ever-increasing volume of data. |
Subject |
Nonmydriatic Fundus; Machine Learning |
Date |
2022-03 |
Language |
eng |
Format |
application/pdf |
Type |
Text |
Publication Type |
Journal Article |
Source |
Journal of Neuro-Ophthalmology, March 2022, Volume 42, Issue 1 |
Collection |
Neuro-Ophthalmology Virtual Education Library: Journal of Neuro-Ophthalmology Archives: https://novel.utah.edu/jno/ |
Publisher |
Lippincott, Williams & Wilkins |
Holding Institution |
Spencer S. Eccles Health Sciences Library, University of Utah |
Rights Management |
© North American Neuro-Ophthalmology Society |
ARK |
ark:/87278/s68q0ccf |
Setname |
ehsl_novel_jno |
ID |
2197437 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s68q0ccf |