Deep learning for retinal non-perfusion and foveal avascular zone analysis in wide-field OCTA in diabetic retinopathy.

We developed an automated framework for segmenting low-quality and non-perfusion areas in widefield OCTA images to obtain two key metrics useful for diabetic retinopathy (DR) monitoring: the retinal non-perfusion index (NPI) and foveal avascular zone (FAZ) area. Using 170 images from 88 patients in the EVIRED cohort, we trained two models: Q-NET, which segments low-quality areas, and NPA-NET, which detects non-perfusion areas and the FAZ. Their combined outputs created a 4-class map to calculate NPI and FAZ area.

Applications of artificial intelligence to inherited retinal diseases: A systematic review.

Artificial intelligence(AI)-based methods have been extensively used for the detection and management of various common retinal conditions, but their targeted development for inherited retinal diseases (IRD) is still nascent. In the context of limited availability of retinal subspecialists, genetic testing and genetic counseling, there is a high need for accurate and accessible diagnostic methods. The currently available AI studies, aiming for detection, classification, and prediction of IRD, remain mainly retrospective and include relatively limited numbers of patients due to their scarcity.

Visual Field Prognosis From Macula and Circumpapillary Spectral Domain Optical Coherence Tomography.

Purpose: To explore the structural-functional loss relationship from optic-nerve-head- and macula-centred spectral-domain (SD) Optical Coherence Tomography (OCT) images in the full spectrum of glaucoma patients using deep-learning methods.

Methods: A cohort comprising 5238 unique eyes classified as suspects or diagnosed with glaucoma was considered. All patients underwent ophthalmologic examination consisting of standard automated perimetry (SAP), macular OCT, and peri-papillary OCT on the same day.

Learning how to robustly estimate camera pose in endoscopic videos.

Purpose: Surgical scene understanding plays a critical role in the technology stack of tomorrow's intervention-assisting systems in endoscopic surgeries. For this, tracking the endoscope pose is a key component, but remains challenging due to illumination conditions, deforming tissues and the breathing motion of organs.

Method: We propose a solution for stereo endoscopes that estimates depth and optical flow to minimize two geometric losses for camera pose estimation.

Evaluation of an Artificial Intelligence-Based Detector of Sub- and Intraretinal Fluid on a Large Set of Optical Coherence Tomography Volumes in Age-Related Macular Degeneration and Diabetic Macular Edema.

Introduction: In this retrospective cohort study, we wanted to evaluate the performance and analyze the insights of an artificial intelligence (AI) algorithm in detecting retinal fluid in spectral-domain OCT volume scans from a large cohort of patients with neovascular age-related macular degeneration (AMD) and diabetic macular edema (DME).

Methods: A total of 3,981 OCT volumes from 374 patients with AMD and 11,501 OCT volumes from 811 patients with DME were acquired with Heidelberg-Spectralis OCT device (Heidelberg Engineering Inc., Heidelberg, Germany) between 2013 and 2021.

Machine Learning Can Predict Anti-VEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVO Associated Macular Edema.

Purpose: To assess the potential of machine learning to predict low and high treatment demand in real life in patients with neovascular age-related macular degeneration (nAMD), retinal vein occlusion (RVO), and diabetic macular edema (DME) treated according to a treat-and-extend regimen (TER).

Design: Retrospective cohort study.

Participants: Three hundred seventy-seven eyes (340 patients) with nAMD and 333 eyes (285 patients) with RVO or DME treated with anti-vascular endothelial growth factor agents (VEGF) according to a predefined TER from 2014 through 2018.