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Cynomolgus monkey's retina volume reference database based on hybrid deep learning optical coherence tomography

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Summary

This study establishes a reference database for cynomolgus monkey retinal volumes using optical coherence tomography. Monkey origin and sex significantly impact retinal volumes, crucial for pre-clinical ocular research.

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Area of Science:

  • Ophthalmology and Pre-clinical Research
  • Primate Models in Vision Science
  • Retinal Imaging and Morphometry

Background:

  • Cynomolgus monkeys (Macaca fascicularis) are vital in pre-clinical ocular studies.
  • Limited data exists on normal macaque retinal morphology and variation.
  • Establishing a comprehensive reference database is essential for accurate research.

Purpose of the Study:

  • To investigate variations in retinal volumes of healthy cynomolgus monkeys.
  • To determine the effects of sex, origin, and eye side on retinal volumes.
  • To create a comprehensive reference database for macaque retinal morphology.

Main Methods:

  • Utilized optical coherence tomography (OCT) for high-resolution retinal imaging.
  • Employed a machine-learning algorithm for automated retinal segmentation.
  • Applied classical computer vision to identify the foveolar depression's deepest point.

Main Results:

  • Overall foveolar mean volume in zone 1 was 0.205 mm³ (CV 7.9%).
  • Retinal volumes generally showed low variation, but significant differences were found based on monkey origin.
  • Monkey sex significantly impacted paracentral retinal volume.

Conclusions:

  • Monkey origin and sex are critical factors influencing retinal volumes in cynomolgus monkeys.
  • These findings necessitate consideration of origin and sex in interpreting macaque retinal data.
  • The established database provides a valuable resource for pre-clinical ocular research.