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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Deep learning in ophthalmology: a review.

Parampal S Grewal1, Faraz Oloumi2, Uriel Rubin1

  • 1Department of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, Alta.

Canadian Journal of Ophthalmology. Journal Canadien D'Ophtalmologie
|August 19, 2018
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This summary is machine-generated.

Deep learning shows promise in ophthalmology for diagnosing conditions like glaucoma and diabetic retinopathy using various imaging techniques. This technology is rapidly advancing and expected to become a key part of eye care.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning is an emerging technology with significant potential in ophthalmology.
  • Current applications span various diagnostic modalities like digital photographs, optical coherence tomography, and visual fields.

Purpose of the Study:

  • To review the current evidence of deep learning applications in ophthalmology.
  • To discuss the future potential and drawbacks of deep learning in ophthalmic care.

Main Methods:

  • Review of existing literature on deep learning in ophthalmology.
  • Analysis of deep learning's utility in assessing ophthalmic diseases.

Main Results:

  • Deep learning tools have demonstrated effectiveness in diagnosing conditions such as cataracts, glaucoma, age-related macular degeneration, and diabetic retinopathy.
  • Evidence supports the utility of deep learning across different diagnostic imaging types.

Conclusions:

  • Deep learning techniques are rapidly evolving and poised for greater integration into routine ophthalmic practice.
  • While promising, potential drawbacks and future applications require continued investigation.