Detection of Disease Features on Retinal OCT Scans Using RETFound.

Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features.

Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach.

To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach.

Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.

Objectives: Various imaging features on optical coherence tomography (OCT) are crucial for identifying and defining disease progression. Establishing a consensus on these imaging features is essential, particularly for training deep learning models for disease classification. This study aims to analyze the inter-rater reliability in labeling the quality and common imaging signatures of retinal OCT scans.

Three-Dimensional Choroidal Vessels Assessment in Age-Related Macular Degeneration.

Purpose: To compare the choroidal vasculature in eyes with early- and intermediate-stage age-related macular degeneration (dAMD) and healthy using a novel three-dimensional algorithm.

Methods: Patients with dAMD and healthy controls underwent clinical examinations and swept-source optical coherence tomography scans (PlexElite-9000 device) centered on the fovea. Scans with quality scores >6 were included.

Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?

Purpose Of Review: This opinion paper highlights the advancements in artificial intelligence (AI) technology for cardiovascular disease (CVD), presents best practices and transformative impacts, and addresses current concerns that must be resolved for broader adoption.

Recent Findings: With the evolution of digitization in data collection, large amounts of data have become available, surpassing the human capacity for processing and analysis, thus enabling the application of AI. These models can learn complex spatial and temporal patterns from large amounts of data, providing patient-specific outputs.