Ophthalmology

Gaze-Based Shared Autonomy Framework With Real-Time Action Primitive Recognition for Robot Manipulators.

Robots capable of robust, real-time recognition of human intent during manipulation tasks could be used to enhance human-robot collaboration for activities of daily living. Eye gaze-based control interfaces offer a non-invasive way to infer intent and reduce the cognitive burden on operators of complex robots. Eye gaze is traditionally used for "gaze triggering" (GT) in which staring at an object,...

Early pigment spot segmentation and classification from iris cellular image analysis with explainable deep learning and multiclass support vector machine.

Globally, retinal disorders impact thousands of individuals. Early diagnosis and treatment of these anomalies might halt their development and prevent many people from developing preventable blindness. Iris spot segmentation is critical due to acquiring iris cellular images that suffer from the off-angle iris, noise, and specular reflection. Most currently used iris segmentation techniques are bas...

Digenic Analysis Finds Highly Interactive Genetic Variants Underlying Polygenic Traits.

We briefly review our recently published approach to mining digenic genotype patterns, which consist of two genotypes each originating in a different ...

Electrical synapses for a pooling layer of the convolutional neural network in retinas.

We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine le...

Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.

Recently, fundus photography (FP) is being increasingly used. Corneal curvature is an essential factor in refractive errors and is associated with sev...

Eye-Gaze-Guided Vision Transformer for Rectifying Shortcut Learning.

Learning harmful shortcuts such as spurious correlations and biases prevents deep neural networks from learning meaningful and useful representations,...

Prediction of activity in eyes with macular neovascularization due to age-related macular degeneration using deep learning.

Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer.

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, highlighting the need for early and accurate detection. In th...

Deep learning visual field global index prediction with optical coherence tomography parameters in glaucoma patients.

The aim of this study was to predict three visual filed (VF) global indexes, mean deviation (MD), pattern standard deviation (PSD), and visual field i...

Shifting Perspectives: A proposed framework for analyzing head-mounted eye-tracking data with dynamic areas of interest and dynamic scenes.

Eye-tracking is a valuable research method for understanding human cognition and is readily employed in human factors research, including human factor...

Exploring the potential of eye tracking on personalized learning and real-time feedback in modern education.

Eye tracking is one of the techniques used to investigate cognitive mechanisms involved in the school context, such as joint attention and visual perc...

Artificial intelligence-enabled quantitative phase imaging methods for life sciences.

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology...

Rapid automated 3-D pose estimation of larval zebrafish using a physical model-trained neural network.

Quantitative ethology requires an accurate estimation of an organism's postural dynamics in three dimensions plus time. Technological progress over th...

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.

The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the leve...

Retina Oculomics in Neurodegenerative Disease.

Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocul...

Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the...

Clinical evaluation of deep learning systems for assisting in the diagnosis of the epiretinal membrane grade in general ophthalmologists.

BACKGROUND: Epiretinal membrane (ERM) is a common age-related retinal disease detected by optical coherence tomography (OCT), with a prevalence of 34....

Deep-learning segmentation method for optical coherence tomography angiography in ophthalmology.

PURPOSE: The optic disc and the macular are two major anatomical structures in the human eye. Optic discs are associated with the optic nerve. Macular...

The effect of optical degradation from cataract using a new Deep Learning optical coherence tomography segmentation algorithm.

PURPOSE: To assess the validity of the results of a freely available online Deep Learning segmentation tool and its sensitivity to noise introduced by...

Natural language processing for identification of refractory status epilepticus in children.

OBJECTIVE: Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high mortality and morbidity. Utilizing electronic hea...