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[Can Big Data change our practices?]
1Service d'ophtalmologique, hôpital Gui De Chauliac, 80, avenue Augustin Fliche, 34295 Montpellier, France; Inserm, epidemiological and clinical research, université Montpellier, 34295 Montpellier, France; The Save Sight Institute, Sydney Medical School, The University of Sydney, Sydney, Australie.
Journal Francais D'Ophtalmologie
|April 14, 2019
Summary
Big Data and Deep Learning offer powerful tools for ophthalmology research, enabling real-world evidence studies on patient care and adverse events. This review explores their applications and limitations in the field.
Area of Science:
- Ophthalmology
- Medical Informatics
- Artificial Intelligence
Background:
- Big Data, defined by Volume, Velocity, and Variety, provides real-world patient care data.
- Deep learning, a subset of machine learning, uses multi-layered algorithms for complex data analysis.
- These technologies are increasingly relevant for medical research and clinical applications.
Purpose of the Study:
- To explore the applications of Big Data and Deep Learning in ophthalmology.
- To review the advantages and disadvantages of these technologies in the ophthalmic field.
- To present a literature review on the uses of Deep Learning in ophthalmology.
Main Methods:
- Literature review of studies utilizing Big Data and Deep Learning in ophthalmology.
- Analysis of the "3 V's" (Volume, Velocity, Variety) of Big Data in the context of eye care.
- Examination of deep learning algorithms and their architectural compositions.
Main Results:
- Big Data facilitates pharmacoepidemiology and adverse event studies in ophthalmology.
- Deep learning models can analyze complex ophthalmic data for various applications.
- The article highlights both the potential benefits and challenges of implementing these technologies.
Conclusions:
- Big Data and Deep Learning hold significant promise for advancing ophthalmic research and patient care.
- Understanding the advantages and disadvantages is crucial for effective implementation.
- Further research and literature synthesis are needed to fully leverage these tools in ophthalmology.
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