Latest AI and machine learning research in ophthalmology for healthcare professionals.
Measuring pupil diameter is vital for gaining insights into physiological and psychological states - traditionally captured by expensive, specialized equipment like Tobii eye-trackers and Pupillabs glasses. This paper presents a novel application that enables pupil diameter estimation using standard webcams, making the process accessible in everyday environments without specialized equipment. Ou...
Deep convolutional neural networks (DCNNs) have demonstrated excellent performance in object recognition and have been found to share some similarities with brain visual processing. However, the substantial gap between DCNNs and human visual perception still exists. Functional magnetic resonance imaging (fMRI) as a widely used technique in cognitive neuroscience can record neural activation in t...
Artificial intelligence (AI) has demonstrated revolutionary potential and wide-ranging applications in the comprehensive management of fundus diseases...
In this paper, we address a recent trend in robotic home appliances to include vision systems on personal devices, capable of personalizing the appl...
Eye movements provide a window into human behaviour, attention, and interaction dynamics. Challenges in real-world, multi-person environments have, ...
Deep learning has achieved impressive performance across various medical imaging tasks. However, its inherent bias against specific groups hinders i...
We present See-Through Face Display, an eye-contact display system designed to enhance gaze awareness in both human-to-human and human-to-avatar com...
Medical images and radiology reports are crucial for diagnosing medical conditions, highlighting the importance of quantitative analysis for clinica...
Social computing scholars have long known that people do not interact with knowledge in straightforward ways, especially in digital environments. Wh...
Advanced cognition can be extracted from the human brain using brain-computer interfaces. Integrating these interfaces with computer vision techniqu...
PURPOSE: Several machine learning studies have used optical coherence tomography (OCT) for multiple sclerosis (MS) classification with promising outco...
PURPOSE: Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, ...
PURPOSE: AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, sel...
This paper proposes a deep-learning computer vision algorithm to estimate hand roll angles for metric-based assessment of surgical suturing skills. Th...
Diagnosing choroidal nevus in color fundus images is challenging for clinicians not regularly practicing it. Machine learning (ML) has proven effectiv...
Retinal lesion is a cause of age-related macular degeneration that poses a big threat to elderly population. The accurate detection and segmentation o...
The electroretinogram (ERG) is an ophthalmic electrophysiology test designed to objectively measure the electrical response of the photoreceptor cells...
Blindness and other eye diseases are a global health concern, particularly in low- and middle-income countries like India. In this regard, during the ...
Conventional deep learning models deal with images one-by-one, requiring costly and time-consuming expert labeling in the field of medical imaging, an...
Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to vision impairment or even blindness if not detected and treated in th...