Ophthalmology

A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification.

The eye affords a unique opportunity to inspect a rich part of the human microvasculature non-invasively via retinal imaging. Retinal blood vessel segmentation and classification are prime steps for the diagnosis and risk assessment of microvascular and systemic diseases. A high volume of techniques based on deep learning have been published in recent years. In this context, we review 158 papers p...

Making eye contact with a robot: Psychophysiological responses to eye contact with a human and with a humanoid robot.

Previous research has shown that eye contact, in human-human interaction, elicits increased affective and attention related psychophysiological responses. In the present study, we investigated whether eye contact with a humanoid robot would elicit these responses. Participants were facing a humanoid robot (NAO) or a human partner, both physically present and looking at or away from the participant...

Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns.

Recent advances in artificial intelligence, particularly in the field of deep learning, have enabled researchers to create compelling algorithms for m...

Image-based phenotyping of disaggregated cells using deep learning.

The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling...

Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.

We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists with...

Interpretation of Depression Detection Models via Feature Selection Methods.

Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatica...

Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still's Disease: A Machine Learning Study.

Adult-onset Still's disease (AOSD) is an autoinflammatory disease with multisystem involvement. Early identification of patients with severe complicat...

Artificial intelligence CT screening model for thyroid-associated ophthalmopathy and tests under clinical conditions.

PURPOSE: Thyroid-associated ophthalmopathy (TAO) might lead to blindness and orbital deformity. The early diagnosis and treatment are conducive to con...

Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.

We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch's membrane opening-minimum rim width (BMO-MRW), peripap...

Gravitational Laws of Focus of Attention.

The understanding of the mechanisms behind focus of attention in a visual scene is a problem of great interest in visual perception and computer visio...

3D shape estimation in a constraint optimization neural network.

One of the most important aspects of visual perception is the inference of 3D shape from a 2D retinal image of the outside world. The existence of sev...

Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning.

Eye movements are disrupted in many neurodegenerative diseases and are frequent and early features in conditions affecting the cerebellum. Characteriz...

Deep phenotype unsupervised machine learning revealed the significance of pachychoroid features in etiology and visual prognosis of age-related macular degeneration.

Unsupervised machine learning has received increased attention in clinical research because it allows researchers to identify novel and objective view...

Early Emergence of Solid Shape Coding in Natural and Deep Network Vision.

Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in ...

CS-Net: Deep learning segmentation of curvilinear structures in medical imaging.

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in auto...

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.

Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a techni...

Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs.

To demonstrate the identification of corneal diseases using a novel deep learning algorithm. A novel hierarchical deep learning network, which is comp...

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images.

PURPOSE: The purpose of this study was to evaluate the ability to align two types of retinal images taken on different platforms; color fundus (CF) ph...

Spike Encoding with Optic Sensory Neurons Enable a Pulse Coupled Neural Network for Ultraviolet Image Segmentation.

Drawing inspiration from biology, neuromorphic systems are of great interest in direct interaction and efficient processing of analogue signals in the...

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.

PURPOSE: This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting gl...