Artificial intelligence in retinal imaging: current status and future prospects.

Introduction: The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

Linking Function and Structure with ReSensNet: Predicting Retinal Sensitivity from OCT using Deep Learning.

Purpose: The currently used measures of retinal function are limited by being subjective, nonlocalized, or taxing for patients. To address these limitations, we sought to develop and evaluate a deep learning (DL) method to automatically predict the functional end point (retinal sensitivity) based on structural OCT images.

Design: Retrospective, cross-sectional study.

Impact of Intra- and Subretinal Fluid on Vision Based on Volume Quantification in the HARBOR Trial.

Purpose: To investigate the functional associations of intraretinal fluid (IRF) and subretinal fluid (SRF) volumes at baseline and after the loading dose as well as fluid change after the first injection with best-corrected visual acuity (BCVA) in patients with neovascular age-related macular degeneration (nAMD) who received an anti-VEGF treatment over 24 months.

Design: Post hoc analysis of a phase III, randomized, multicenter trial in which ranibizumab was administered monthly or in a pro re nata regimen (HARBOR).

Participants: Study eyes of 1094 treatment-naïve patients with nAMD.

Fundus autofluorescence and optical coherence tomography biomarkers associated with the progression of geographic atrophy secondary to age-related macular degeneration.

Objectives: To investigate the impact of qualitatively graded and deep learning quantified imaging biomarkers on growth of geographic atrophy (GA) secondary to age-related macular degeneration.

Methods: This prospective study included 1062 visits of 181 eyes of 100 patients with GA. Spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) images were acquired at each visit.

Spatio-temporal alterations in retinal and choroidal layers in the progression of age-related macular degeneration (AMD) in optical coherence tomography.

Age-related macular degeneration (AMD) is the predominant cause of vision loss in the elderly with a major impact on ageing societies and healthcare systems. A major challenge in AMD management is the difficulty to determine the disease stage, the highly variable progression speed and the risk of conversion to advanced AMD, where irreversible functional loss occurs. In this study we developed an optical coherence tomography (OCT) imaging based spatio-temporal reference frame to characterize the morphologic progression of intermediate age-related macular degeneration (AMD) and to identify distinctive patterns of conversion to the advanced stages macular neovascularization (MNV) and macular atrophy (MA).

A novel proline substitution (Arg201Pro) in alpha helix 8 of TMEM98 causes autosomal dominant nanophthalmos-4, closed angle glaucoma and attenuated visual acuity.

Nanophthalmos-4 is a rare autosomal dominant disorder caused by two known variations in TMEM98. An Austrian Caucasian pedigree was identified suffering from nanophthalmos and late onset angle-closure glaucoma and premature loss of visual acuity. Whole exome sequencing identified segregation of a c.

[Artificial intelligence in ophthalmology : Guidelines for physicians for the critical evaluation of studies].

Background: Empirical models have been an integral part of everyday clinical practice in ophthalmology since the introduction of the Sanders-Retzlaff-Kraff (SRK) formula. Recent developments in the field of statistical learning (artificial intelligence, AI) now enable an empirical approach to a wide range of ophthalmological questions with an unprecedented precision.

Objective: Which criteria must be considered for the evaluation of AI-related studies in ophthalmology?

Material And Methods: Exemplary prediction of visual acuity (continuous outcome) and classification of healthy and diseased eyes (discrete outcome) using retrospectively compiled optical coherence tomography data (50 eyes of 50 patients, 50 healthy eyes of 50 subjects).

Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.

Artificial intelligence has recently made a disruptive impact in medical imaging by successfully automatizing expert-level diagnostic tasks. However, replicating human-made decisions may inherently be biased by the fallible and dogmatic nature of human experts, in addition to requiring prohibitive amounts of training data. In this paper, we introduce an unsupervised deep learning architecture particularly designed for OCT representations for unbiased, purely data-driven biomarker discovery.

Focal Scleral Nodule: A New Name for Solitary Idiopathic Choroiditis and Unifocal Helioid Choroiditis.

Purpose: To evaluate multimodal imaging findings of solitary idiopathic choroiditis (SIC; also known as unifocal helioid choroiditis) to clarify its origin, anatomic location, and natural course.

Design: Multicenter retrospective observational case series.

Participants: Sixty-three patients with SIC in 1 eye.

Characterization of Drusen and Hyperreflective Foci as Biomarkers for Disease Progression in Age-Related Macular Degeneration Using Artificial Intelligence in Optical Coherence Tomography.

Importance: The morphologic changes and their pathognomonic distribution in progressing age-related macular degeneration (AMD) are not well understood.

Objectives: To characterize the pathognomonic distribution and time course of morphologic patterns in AMD and to quantify changes distinctive for progression to macular neovascularization (MNV) and macular atrophy (MA).

Design, Setting, And Participants: This cohort study included optical coherence tomography (OCT) volumes from study participants with early or intermediate AMD in the fellow eye in the HARBOR (A Study of Ranibizumab Administered Monthly or on an As-needed Basis in Patients With Subfoveal Neovascular Age-Related Macular Degeneration) trial.

Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning.

Diabetic macular edema (DME) and retina vein occlusion (RVO) are macular diseases in which central photoreceptors are affected due to pathological accumulation of fluid. Optical coherence tomography allows to visually assess and evaluate photoreceptor integrity, whose alteration has been observed as an important biomarker of both diseases. However, the manual quantification of this layered structure is challenging, tedious and time-consuming.

[Second opinion in retinal imaging].

With the use of digital imaging systems and the possibilities of data exchange, the second opinion is becoming increasingly more important in retinal imaging. For a meaningful application, technical imaging requirements and medical assessment quality requirements have to be fulfilled. Responsibilities should be clearly defined.

[Potential of methods of artificial intelligence for quality assurance].

Background: Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data.

Objective: This article discusses to what extent the application of AI algorithms can contribute to quality assurance in the field of ophthalmology.

Methods: Relevant aspects from the literature are discussed.

Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina.

Diagnosis and treatment in ophthalmology depend on modern retinal imaging by optical coherence tomography (OCT). The recent staggering results of machine learning in medical imaging have inspired the development of automated segmentation methods to identify and quantify pathological features in OCT scans. These models need to be sensitive to image features defining patterns of interest, while remaining robust to differences in imaging protocols.

TOPOGRAPHIC ANALYSIS OF PHOTORECEPTOR LOSS CORRELATED WITH DISEASE MORPHOLOGY IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

Purpose: To quantify morphologic photoreceptor integrity during anti-vascular endothelial growth factor (anti-VEGF) therapy of neovascular age-related macular degeneration and correlate these findings with disease morphology and function.

Methods: This presents a post hoc analysis on spectral-domain optical coherence tomography data of 185 patients, acquired at baseline, Month 3, and Month 12 in a multicenter, prospective trial. Loss of the ellipsoid zone (EZ) was manually quantified in all optical coherence tomography volumes.

Effect of posterior vitreous detachment on treat-and-extend versus monthly ranibizumab for neovascular age-related macular degeneration.

Aims: To investigate the impact of posterior vitreous detachment (PVD) on the efficacy of treat-and-extend (T&E) ranibizumab in neovascular age-related macular degeneration.

Methods: In a post hoc analysis of a randomised controlled clinical trial, spectral-domain optical coherence tomography images of treatment-naïve patients randomised to receive T&E (n=265) or monthly (n=264) ranibizumab for 12 months were included. Certified, masked graders diagnosed the presence or the absence of complete PVD.

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT.

Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a priori definitions of these regions, large-scale annotations, and a representative patient cohort in the training set. In contrast, anomaly detection is not limited to specific definitions of pathologies and allows for training on healthy samples without annotation.

MORPHOLOGICAL AND FUNCTIONAL CHARACTERISTICS AT THE ONSET OF EXUDATIVE CONVERSION IN AGE-RELATED MACULAR DEGENERATION.

Purpose: To characterize retinal morphology differences among different types of choroidal neovascularization and visual function changes at the onset of exudative age-related macular degeneration.

Methods: In a post hoc analysis of a prospective clinical study, 1,097 fellow eyes from subjects with choroidal neovascularization in the study eye enrolled in the HARBOR trial were evaluated. The onset of exudation was diagnosed on monthly optical coherence tomography by two masked graders.