AI-based monitoring of retinal fluid in disease activity and under therapy. (January 2022)

• Record Type: Journal Article
• Authors: Schmidt-Erfurth, Ursula; Reiter, Gregor S.; Riedl, Sophie; Seeböck, Philipp; Vogl, Wolf-Dieter; Blodi, Barbara A.; Domalpally, Amitha; Fawzi, Amani; Jia, Yali; Sarraf, David; Bogunović, Hrvoje
• Is Part Of: Progress in retinal and eye research. Volume 86(2022)
• Publication Date: 2022-01
• Subjects: Optical coherence tomography (OCT) -- Deep learning (DL) -- Intraretinal fluid (IRF) -- Subretinal fluid (SRF) -- Fluid/function correlation -- Automated algorithms; Retina -- Periodicals; Retina -- Research -- Methodology -- Periodicals; Eye -- Diseases -- Periodicals; Eye -- Periodicals; Eye Diseases -- Periodicals; Retina -- Periodicals; Rétine -- Périodiques; Rétine -- Recherche -- Méthodologie -- Périodiques; 617.7005
• DOI: 10.1016/j.preteyeres.2021.100972
• Languages: English
• ISSNs: 1350-9462

Abstract

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. Highlights: The current assessment of optical coherence tomography images in clinical practice is unsatisfactory. Artificial intelligence offers objective and precise localization and quantification of fluid. Automated algorithms provide identification of different types and dynamics of fluid resolution. The performance of advanced AI tools is superior to human experts in terms of accuracy and speed. AI in retinal fluid analysis introduces precision medicine into real-world patient management.