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.

Virtual Reality-Based and Conventional Visual Field Examination Comparison in Healthy and Glaucoma Patients.

Purpose: Clinically evaluate the noninferiority of a custom virtual reality (VR) perimetry system when compared to a clinically and routinely used perimeter on both healthy subjects and glaucoma patients.

Methods: We use a custom-designed VR perimetry system tailored for visual field testing. The system uses Oculus Quest VR headset (Facebook Technologies, LLC, Bern, Switzerland), that includes a clicker for participant response feedback.

Comparative Study Between the SORS and Dynamic Strategy Visual Field Testing Methods on Glaucomatous and Healthy Subjects.

Purpose: To clinically validate the noninferiority of the sequentially optimized reconstruction strategy (SORS) when compared to the dynamic strategy (DS).

Methods: SORS is a novel perimetry testing strategy that evaluates a subset of test locations of a visual field (VF) test pattern and estimates the untested locations by linear approximation. When testing fewer locations, SORS has been shown in computer simulations to bring improvements in speed over conventional perimetry tests, while maintaining acquisition at high-quality acquisition.

Patient-attentive sequential strategy for perimetry-based visual field acquisition.

Perimetry is a non-invasive clinical psychometric examination used for diagnosing ophthalmic and neurological conditions. At its core, perimetry relies on a subject pressing a button whenever they see a visual stimulus within their field of view. This sequential process then yields a 2D visual field image that is critical for clinical use.

A deep learning approach to automatic detection of early glaucoma from visual fields.

Purpose: To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination.

Method: Two data sets of VFs acquired with the OCTOPUS 101 G1 program and the Humphrey Field Analyzer 24-2 pattern were subdivided into control and early-glaucomatous groups, and converted into a new image using a novel voronoi representation to train a custom-designed CNN so to discriminate between control and early-glaucomatous eyes.

Sequentially optimized reconstruction strategy: A meta-strategy for perimetry testing.

Perimetry testing is an automated method to measure visual function and is heavily used for diagnosing ophthalmic and neurological conditions. Its working principle is to sequentially query a subject about perceived light using different brightness levels at different visual field locations. At a given location, this query-patient-feedback process is expected to converge at a perceived sensitivity, such that a shown stimulus intensity is observed and reported 50% of the time.

Spatial Entropy Pursuit for Fast and Accurate Perimetry Testing.

Purpose: To propose a static automated perimetry strategy that increases the speed of visual field (VF) evaluation while retaining threshold estimate accuracy.

Methods: We propose a novel algorithm, spatial entropy pursuit (SEP), which evaluates individual locations by using zippy estimation by sequential testing (ZEST) but additionally uses neighboring locations to estimate the sensitivity of related locations. We model the VF with a conditional random field (CRF) where each node represents a location estimate that depends on itself as well as its neighbors.

Automatic assessment of time-resolved OCT images for selective retina therapy.

Purpose: In recent years, selective retina laser treatment (SRT), a sub-threshold therapy method, avoids widespread damage to all retinal layers by targeting only a few. While these methods facilitate faster healing, their lack of visual feedback during treatment represents a considerable shortcoming as induced lesions remain invisible with conventional imaging and make clinical use challenging. To overcome this, we present a new strategy to provide location-specific and contact-free automatic feedback of SRT laser applications.