Normative prospective data on automatically quantified retinal morphology correlated to retinal function in healthy ageing eyes by two microperimetry devices.

Purpose: The relationship between retinal morphology, as assessed by optical coherence tomography (OCT), and retinal function in microperimetry (MP) has not been well studied, despite its increasing importance as an essential functional endpoint for clinical trials and emerging therapies in retinal diseases. Normative databases of healthy ageing eyes are largely missing from literature.

Methods: Healthy subjects above 50 years were examined using two MP devices, MP-3 (NIDEK) and MAIA (iCare).

Artificial intelligence in assessing progression of age-related macular degeneration.

The human population is steadily growing with increased life expectancy, impacting the prevalence of age-dependent diseases, including age-related macular degeneration (AMD). Health care systems are confronted with an increasing burden with rising patient numbers accompanied by ongoing developments of therapeutic approaches. Concurrent advances in imaging modalities provide eye care professionals with a large amount of data for each patient.

Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration.

To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical coherence tomography (OCT) in intermediate AMD (iAMD). Patients with iAMD were examined by OCT (Spectralis). DL-based algorithms quantified ellipsoid zone (EZ)-thickness, hyperreflective foci (HRF) and drusen volume.

Repeatability of Microperimetry in areas of RPE and Photoreceptor loss in Geographic Atrophy supported by AI-based OCT biomarker quantification.

Purpose: Growing interest in microperimetry (MP) or fundus-controlled perimetry (FCP) as targeted psychometric testing method in geographic atrophy (GA) is warranted due to the disease subclinical/extra-foveal appearance or preexisting foveal loss with visual acuity becoming unreliable. We provide comprehensive pointwise test-retest repeatability reference values on the most widely used MP devices and combine them with targeted testing in areas of retinal pigment epithelium (RPE) as well as photoreceptor (PR) integrity loss, guiding the interpretation of sensitivity loss during the long-term follow-up of GA patients.

Design: Prospective reliability study METHODS: Patients with GA underwent consecutive testing on CenterVue (iCare) MAIA and NIDEK MP3 devices.

Correlation of retinal fluid and photoreceptor and RPE loss in neovascular AMD by automated quantification, a real-world FRB! analysis.

Purpose: To quantify ellipsoid zone (EZ) loss during anti-VEGF therapy for neovascular age-related macular degeneration (nAMD) and correlate these findings with nAMD disease activity using artificial intelligence-based algorithms.

Methods: Spectral domain optical coherence tomography (Spectralis, Heidelberg Engineering) images from nAMD treatment-naïve patients from the Fight Retinal Blindness! (FRB!) Registry from Zürich, Switzerland were processed at baseline and over 3 years of follow-up. An approved deep learning algorithm (Fluid Monitor, RetInSight) was used to automatically quantify intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED).

A Novel Management Challenge in Age-Related Macular Degeneration: Artificial Intelligence and Expert Prediction of Geographic Atrophy.

Purpose: The progression of geographic atrophy (GA) secondary to age-related macular degeneration is highly variable among individuals. Prediction of the progression is critical to identify patients who will benefit most from the first treatments currently approved. The aim of this study was to investigate the value and difference in predictive power between ophthalmologists and artificial intelligence (AI) in reliably assessing individual speed of GA progression.

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.

Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In ophthalmology, large multi-modal datasets are abundantly available and conveniently accessible as modern retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography (OCT) scans to assess the eye.

VLFATRollout: Fully transformer-based classifier for retinal OCT volumes.

Background And Objective: Despite the promising capabilities of 3D transformer architectures in video analysis, their application to high-resolution 3D medical volumes encounters several challenges. One major limitation is the high number of 3D patches, which reduces the efficiency of the global self-attention mechanisms of transformers. Additionally, background information can distract vision transformers from focusing on crucial areas of the input image, thereby introducing noise into the final representation.

AI in the clinical management of GA: A novel therapeutic universe requires novel tools.

Regulatory approval of the first two therapeutic substances for the management of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) is a major breakthrough following failure of numerous previous trials. However, in the absence of therapeutic standards, diagnostic tools are a key challenge as functional parameters in GA are hard to provide. The majority of anatomical biomarkers are subclinical, necessitating advanced and sensitive image analyses.

Pretraining of 3D image segmentation models for retinal OCT using denoising-based self-supervised learning.

Deep learning algorithms have allowed the automation of segmentation for many biomarkers in retinal OCTs, enabling comprehensive clinical research and precise patient monitoring. These segmentation algorithms predominantly rely on supervised training and specialised segmentation networks, such as U-Nets. However, they require segmentation annotations, which are challenging to collect and require specialized expertise.

Role of Artificial Intelligence in Retinal Diseases.

Artificial intelligence (AI) has already found its way into ophthalmology, with the first approved algorithms that can be used in clinical routine. Retinal diseases in particular are proving to be an important area of application for AI, as they are the main cause of blindness and the number of patients suffering from retinal diseases is constantly increasing. At the same time, regular imaging using high-resolution modalities in a standardised and reproducible manner generates immense amounts of data that can hardly be processed by human experts.

Artificial intelligence for geographic atrophy: pearls and pitfalls.

Purpose Of Review: This review aims to address the recent advances of artificial intelligence (AI) in the context of clinical management of geographic atrophy (GA), a vision-impairing late-stage manifestation of age-related macular degeneration (AMD).

Recent Findings: Recent literature shows substantial advancements in the development of AI systems to segment GA lesions on multimodal retinal images, including color fundus photography (CFP), fundus autofluorescence (FAF) and optical coherence tomography (OCT), providing innovative solutions to screening and early diagnosis. Especially, the high resolution and 3D-nature of OCT has provided an optimal source of data for the training and validation of novel algorithms.

Quantitative comparison of automated OCT and conventional FAF-based geographic atrophy measurements in the phase 3 OAKS/DERBY trials.

With the approval of the first two substances for the treatment of geographic atrophy (GA) secondary to age-related macular degeneration (AMD), a standardized monitoring of patients treated with complement inhibitors in clinical practice is needed. Optical coherence tomography (OCT) provides high-resolution access to the retinal pigment epithelium (RPE) and neurosensory layers, such as the ellipsoid zone (EZ), which further enhances the understanding of disease progression and therapeutic effects in GA compared to conventional fundus autofluorescence (FAF). In addition, artificial intelligence-based methodology allows the identification and quantification of GA-related pathology on OCT in an objective and standardized manner.

Association of microaneurysms with retinal vascular alterations in patients with retinal vein occlusion.

Objective: To investigate the localization, distribution, and type of central microaneurysms (MAs) and their relationship with retinal vascular alterations in patients with retinal vein occlusion (RVO).

Methods: In this cross-sectional study, ultra-widefield color fundus photography (UWF-CF), standard and single-capture 65° widefield (WF) optical coherence tomography angiography (OCTA) were performed in consecutive patients with RVO treated at the Department of Ophthalmology and Optometry, Medical University of Vienna. UWF-CF, en face and B-Scans in 6 mm × 6 mm OCTA were examined for detection of MAs.

An Analysis of Ocular Biometrics: A Comprehensive Retrospective Study in a Large Cohort of Pediatric Cataract Patients.

This study aims to provide a comprehensive analysis of ocular biometric parameters in pediatric patients with cataracts to optimize surgical outcomes. By evaluating various biometric data, we seek to enhance the decision-making process for intraocular lens (IOL) placement, particularly with advanced technologies like femtosecond lasers. This retrospective comparative study included pediatric patients with cataracts who underwent ocular biometric measurements and cataract extraction with anterior vitrectomy at the Medical University of Vienna between January 2019 and December 2021.

Linking disease activity with optical coherence tomography angiography in neovascular age related macular degeneration using artificial intelligence.

To investigate quantitative associations between AI-assessed disease activity and optical coherence tomography angiography (OCTA)-derived parameters in patients with neovascular age-related macular degeneration (nAMD) undergoing anti-VEGF therapy. OCTA and SD-OCT images obtained from multicenter, randomized study data were evaluated. A deep learning algorithm (RetInSight) was used to detect and quantify macular fluid on SD-OCT.

Metadata-enhanced contrastive learning from retinal optical coherence tomography images.

Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues.

Disease Activity and Therapeutic Response to Pegcetacoplan for Geographic Atrophy Identified by Deep Learning-Based Analysis of OCT.

Purpose: To quantify morphological changes of the photoreceptors (PRs) and retinal pigment epithelium (RPE) layers under pegcetacoplan therapy in geographic atrophy (GA) using deep learning-based analysis of OCT images.

Design: Post hoc longitudinal image analysis.

Participants: Patients with GA due to age-related macular degeneration from 2 prospective randomized phase III clinical trials (OAKS and DERBY).

Vitrectomy versus scleral buckle for retinal detachment without posterior vitreous detachment.

To compare the effectiveness and safety of scleral buckling and pars plana vitrectomy in treating retinal detachment without posterior vitreous detachment. A total of 88 eyes of 83 patients with retinal detachment without prior posterior vitreous detachment were investigated retrospectively. Group A comprised patients who underwent scleral buckling (n = 47) and Group B (n = 36) patients who were treated with pars plana vitrectomy.

ADVANCES IN PHOTORECEPTOR AND RETINAL PIGMENT EPITHELIUM QUANTIFICATIONS IN INTERMEDIATE AGE-RELATED MACULAR DEGENERATION: High-Res Versus Standard SPECTRALIS Optical Coherence Tomography.

Purpose: In this study, differences in retinal feature visualization of high-resolution optical coherence tomography (OCT) devices were investigated with different axial resolutions in quantifications of retinal pigment epithelium and photoreceptors (PRs) in intermediate age-related macular degeneration.

Methods: Patients were imaged with standard SPECTRALIS HRA + OCT and the investigational High-Res OCT device (both by Heidelberg Engineering, Heidelberg, Germany). Drusen, retinal pigment epithelium, and PR layers were segmented using validated artificial intelligence-based algorithms followed by manual corrections.

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning.

Purpose: Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on optical coherence tomography imaging.

Methods: In this longitudinal observational study, individuals with bilateral iAMD participating in a multicenter clinical trial were screened for OPL subsidence and RPE and outer retinal atrophy. OPL subsidence was segmented on an A-scan basis in optical coherence tomography volumes, obtained 6-monthly with 36 months follow-up.