Artificial intelligence in OCT angiography

Tristan T. Hormel, Thomas S. Hwang, Steven T. Bailey, David J. Wilson, David Huang, Yali Jia

Research output: Contribution to journalReview articlepeer-review

32 Scopus citations


Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that provides three-dimensional, information-rich vascular images. With numerous studies demonstrating unique capabilities in biomarker quantification, diagnosis, and monitoring, OCTA technology has seen rapid adoption in research and clinical settings. The value of OCTA imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features and pathology. Today, the most powerful image analysis methods are based on artificial intelligence (AI). While AI encompasses a large variety of techniques, machine-learning-based, and especially deep-learning-based, image analysis provides accurate measurements in a variety of contexts, including different diseases and regions of the eye. Here, we discuss the principles of both OCTA and AI that make their combination capable of answering new questions. We also review contemporary applications of AI in OCTA, which include accurate detection of pathologies such as choroidal neovascularization, precise quantification of retinal perfusion, and reliable disease diagnosis.

Original languageEnglish (US)
Article number100965
JournalProgress in Retinal and Eye Research
StatePublished - Nov 2021


  • Artificial intelligence
  • Deep learning
  • Image analysis
  • OCT Angiography

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems


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