Efficacy and accuracy of artificial intelligence to overlay multimodal images from different optical instruments in patients with retinitis pigmentosa.

Journal: Clinical & experimental ophthalmology
PMID: 37102206

Abstract

BACKGROUND: Retinitis pigmentosa (RP) represents a group of progressive, genetically heterogenous blinding diseases. Recently, relationships between measures of retinal function and structure are needed to help identify outcome measures or biomarkers for clinical trials. The ability to align retinal multimodal images, taken on different platforms, will allow better understanding of this relationship. We investigate the efficacy of artificial intelligence (AI) in overlaying different multimodal retinal images in RP patients.

Authors

  • Shaden H Yassin
    Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.
  • Yiqian Wang
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
  • William R Freeman
    Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
  • Anna Heinke
    Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.
  • Evan Walker
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, California.
  • Truong Nguyen
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
  • Dirk-Uwe G Bartsch
    Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
  • Cheolhong An
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
  • Shyamanga Borooah
    Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, California, USA.

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