Ophthalmology

Multilayered rule-based expert system for diagnosing uveitis.

Uveitis is a condition caused by inflammation of the uvea, which is the middle layer of the eye. Uveitis can result in swelling or destruction of the eye tissue, which can lead to visual impairment or blindness [1]. Many diseases, either systemic or localized to the eye, are associated with the symptoms of uveitis. Thus, it is often hard to determine the underlying disease responsible for uveitis,...

A feature agnostic approach for glaucoma detection in OCT volumes.

Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-d...

A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.

BACKGROUND: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine inter...

Ten Years of Gabor-Domain Optical Coherence Microscopy.

Gabor-domain optical coherence microscopy (GDOCM) is a high-definition imaging technique leveraging principles of low-coherence interferometry, liquid...

Stereo Vision Based Sensory Substitution for the Visually Impaired.

The development of computer vision based systems dedicated to help visually impaired people to perceive the environment, to orientate and navigate has...

A Probabilistic Synapse With Strained MTJs for Spiking Neural Networks.

Spiking neural networks (SNNs) are of interest for applications for which conventional computing suffers from the nearly insurmountable memory-process...

Automatic segmentation of hyperreflective foci in OCT images.

BACKGROUND AND OBJECTIVE: The leading cause of vision loss in the Western World is Age-related Macular Degeneration (AMD), but together with modern me...

Artery-vein segmentation in fundus images using a fully convolutional network.

Epidemiological studies demonstrate that dimensions of retinal vessels change with ocular diseases, coronary heart disease and stroke. Different metri...

Self-supervised iterative refinement learning for macular OCT volumetric data classification.

We present self-supervised iterative refinement learning (SIRL) as a pipeline to improve a type of macular optical coherence tomography (OCT) volumetr...

Acuity VEP: improved with machine learning.

PURPOSE: Acuity-VEP approaches basically all use the information obtained across a number of check sizes (or spatial frequencies) to derive a measure ...

A Review of Machine Learning Techniques for Keratoconus Detection and Refractive Surgery Screening.

Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. These techniques utilize inputs fr...

Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal...

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT.

Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate ...

Tensor decomposition of hyperspectral images to study autofluorescence in age-related macular degeneration.

Autofluorescence is the emission of light by naturally occurring tissue components on the absorption of incident light. Autofluorescence within the ey...

Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology.

The lifestyle of modern society has changed significantly with the emergence of artificial intelligence (AI), machine learning (ML), and deep learning...

Microaneurysm detection in fundus images using a two-step convolutional neural network.

BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in or...

Learning the signatures of the human grasp using a scalable tactile glove.

Humans can feel, weigh and grasp diverse objects, and simultaneously infer their material properties while applying the right amount of force-a challe...

Machine Learning in the Detection of the Glaucomatous Disc and Visual Field.

Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is of utmost importance as there is abundant evidence that early tr...

Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach.

OBJECTIVE: Because of high variability, tear film osmolarity measures have been questioned in dry eye assessment. Understanding the origin of such var...

Automated summarisation of SDOCT volumes using deep learning: Transfer learning vs de novo trained networks.

Spectral-domain optical coherence tomography (SDOCT) is a non-invasive imaging modality that generates high-resolution volumetric images. This modalit...