AI-based monitoring of retinal fluid in disease activity and under therapy

Ursula Schmidt-Erfurth*, Gregor S. Reiter, Sophie Riedl, Philipp Seeböck, Wolf Dieter Vogl, Barbara A. Blodi, Amitha Domalpally, Amani Fawzi, Yali Jia, David Sarraf, Hrvoje Bogunović

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical examination. Artificial intelligence (AI) with its capability to objectively identify, localize and quantify fluid introduces fully automated tools into OCT imaging for personalized disease management. Deep learning performance has already proven superior to human experts, including physicians and certified readers, in terms of accuracy and speed. Reproducible measurement of retinal fluid relies on precise AI-based segmentation methods that assign a label to each OCT voxel denoting its fluid type such as intraretinal fluid (IRF) and subretinal fluid (SRF) or pigment epithelial detachment (PED) and its location within the central 1-, 3- and 6-mm macular area. Such reliable analysis is most relevant to reflect differences in pathophysiological mechanisms and impacts on retinal function, and the dynamics of fluid resolution during therapy with different regimens and substances. Yet, an in-depth understanding of the mode of action of supervised and unsupervised learning, the functionality of a convolutional neural net (CNN) and various network architectures is needed. Greater insight regarding adequate methods for performance, validation assessment, and device- and scanning-pattern-dependent variations is necessary to empower ophthalmologists to become qualified AI users. Fluid/function correlation can lead to a better definition of valid fluid variables relevant for optimal outcomes on an individual and a population level. AI-based fluid analysis opens the way for precision medicine in real-world practice of the leading retinal diseases of modern times.

Original languageEnglish (US)
Article number100972
JournalProgress in Retinal and Eye Research
DOIs
StateAccepted/In press - 2021

Keywords

  • Automated algorithms
  • Deep learning (DL)
  • Fluid/function correlation
  • Intraretinal fluid (IRF)
  • Optical coherence tomography (OCT)
  • Subretinal fluid (SRF)

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems

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