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.

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.

Dynamics and patterns of recurrence in neovascular AMD during real-world management using automated fluid monitoring.

In this retrospective longitudinal observational study, data from one site of the Fight Retinal Blindness! Registry (University of Zurich, Switzerland) was used to investigate the quantity and distribution of recurrent fluid in neovascular age-related macular degeneration (nAMD). Study eye eligibility required treatment-naïve nAMD, receiving at least three anti-vascular endothelial growth factor injections, followed by a treatment discontinuation of at least six months and subsequence fluid recurrence. To quantify fluid, a regulatory approved deep learning algorithm (Vienna Fluid Monitor, RetInSight, Vienna, Austria) was used.

3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression From Longitudinal OCTs.

Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images.

Morph-SSL: Self-Supervision With Longitudinal Morphing for Forecasting AMD Progression From OCT Volumes.

The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL.

An Automated Comparative Analysis of the Exudative Biomarkers in Neovascular Age-Related Macular Degeneration, The RAP Study: Report 6.

Purpose: To investigate differences in volume and distribution of the main exudative biomarkers across all types and subtypes of macular neovascularization (MNV) using artificial intelligence (AI).

Design: Cross-sectional study.

Methods: An AI-based analysis was conducted on 34,528 OCT B-scans consisting of 281 (250 unifocal, 31 multifocal) MNV3, 55 MNV2, and 121 (30 polypoidal, 91 non-polypoidal) MNV1 treatment-naive eyes.

Influence of drusenoid pigment epithelial detachments on the progression of age-related macular degeneration and visual acuity.

Objective: To analyze the presence and morphologic characteristics of drusenoid pigment epithelial detachments (DPEDs) in spectral-domain optical coherence tomography (SD-OCT) in Caucasian patients with early and intermediate age-related macular degeneration (AMD) as well as the influence of these characteristics on best-corrected visual acuity (BCVA) and disease progression.

Design: Prospective observational cohort study.

Participants: 89 eyes of 56 patients with early and intermediate AMD.

Long-term effect of fluid volumes during the maintenance phase in neovascular age-related macular degeneration: results from Fight Retinal Blindness!

Objective: To investigate the effect of macular fluid volumes (subretinal fluid [SRF], intraretinal fluid [IRF], and pigment epithelium detachment [PED]) after initial treatment on functional and structural outcomes in neovascular age-related macular degeneration in a real-world cohort from Fight Retinal Blindness!

Methods: Treatment-naive neovascular age-related macular degeneration patients from Fight Retinal Blindness! (Zürich, Switzerland) were included. Macular fluid on optical coherence tomography was automatically quantified using an approved artificial intelligence algorithm. Follow-up of macular fluid, number of anti-vascular endothelial growth factor treatments, effect of fluid volumes after initial treatment (high, top 25%; low, bottom 75%) on best-corrected visual acuity, and development of macular atrophy and fibrosis was investigated over 48 months.

Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).

Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis.

Analysing early changes of photoreceptor layer thickness following surgery in eyes with epiretinal membranes.

Background/objectives: To analyse short-term changes of mean photoreceptor thickness (PRT) on the ETDRS-grid after vitrectomy and membrane peeling in patients with epiretinal membrane (ERM).

Subjects/methods: Forty-eight patients with idiopathic ERM were included in this prospective study. Study examinations comprised best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) before surgery, 1 week (W1), 1 month (M1) and 3 months (M3) after surgery.

Approved AI-based fluid monitoring to identify morphological and functional treatment outcomes in neovascular age-related macular degeneration in real-world routine.

Aim: To predict antivascular endothelial growth factor (VEGF) treatment requirements, visual acuity and morphological outcomes in neovascular age-related macular degeneration (nAMD) using fluid quantification by artificial intelligence (AI) in a real-world cohort.

Methods: Spectral-domain optical coherence tomography data of 158 treatment-naïve patients with nAMD from the Fight Retinal Blindness! registry in Zurich were processed at baseline, and after initial treatment using intravitreal anti-VEGF to predict subsequent 1-year and 4-year outcomes. Intraretinal and subretinal fluid and pigment epithelial detachment volumes were segmented using a deep learning algorithm (Vienna Fluid Monitor, RetInSight, Vienna, Austria).

Progression Dynamics of Early versus Later Stage Atrophic Lesions in Nonneovascular Age-Related Macular Degeneration Using Quantitative OCT Biomarker Segmentation.

Purpose: To investigate the progression of geographic atrophy secondary to nonneovascular age-related macular degeneration in early and later stage lesions using artificial intelligence-based precision tools.

Design: Retrospective analysis of an observational cohort study.

Subjects: Seventy-four eyes of 49 patients with ≥ 1 complete retinal pigment epithelial and outer retinal atrophy (cRORA) lesion secondary to age-related macular degeneration were included.

VALIDATION OF AN AUTOMATED FLUID ALGORITHM ON REAL-WORLD DATA OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION OVER FIVE YEARS.

Background/purpose: To apply an automated deep learning automated fluid algorithm on data from real-world management of patients with neovascular age-related macular degeneration for quantification of intraretinal/subretinal fluid volumes in optical coherence tomography images.

Methods: Data from the Vienna Imaging Biomarker Eye Study (VIBES, 2007-2018) were analyzed. Databases were filtered for treatment-naive neovascular age-related macular degeneration with a baseline optical coherence tomography and at least one follow-up and 1,127 eyes included.

Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol.

Aims: Age-related macular degeneration (AMD) is characterised by a progressive loss of central vision. Intermediate AMD is a risk factor for progression to advanced stages categorised as geographic atrophy (GA) and neovascular AMD. However, rates of progression to advanced stages vary between individuals.