Enhancing Meibography Image Analysis Through Artificial Intelligence-Driven Quantification and Standardization for Dry Eye Research.

Journal: Translational vision science & technology
PMID: 38904611

Abstract

PURPOSE: This study enhances Meibomian gland (MG) infrared image analysis in dry eye (DE) research through artificial intelligence (AI). It is comprised of two main stages: automated eyelid detection and tarsal plate segmentation to standardize meibography image analysis. The goal is to address limitations of existing assessment methods, bridge the curated and real-world dataset gap, and standardize MG image analysis.

Authors

  • Chun-Hsiao Yeh
    Vision Science Group, Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, Berkeley, CA, USA.
  • Andrew D Graham
    Vision Science Graduate Group, Herbert Wertheim School of Optometry and Vision Science, University of California, 360 Minor Hall, MC#2020, Berkeley, CA, 94720-2020, USA.
  • Stella X Yu
    International Comp. Sci. Inst., UC Berkeley, 1947 Center St, Berkeley, CA, United States.
  • Meng C Lin
    Vision Science Graduate Group, Herbert Wertheim School of Optometry and Vision Science, University of California, 360 Minor Hall, MC#2020, Berkeley, CA, 94720-2020, USA. mlin@berkeley.edu.

Keywords

External Resources

Popular Topics
Recent Journals