Latest AI and machine learning research in ophthalmology for healthcare professionals.
Half a century ago, cerebellar learning models based on a simple perceptron were proposed independently by Marr and Albus. Soon, these models were combined with Ito's flocculus hypothesis that the cerebellar flocculus controls the vestibulo-ocular reflex through teacher signal-dependent learning, and consequently integrated into the so-called Marr-Albus-Ito cerebellar learning hypothesis. Ten year...
Glaucoma is the second leading cause of blindness worldwide. This paper proposes an automated glaucoma screening method using retinal fundus images via the ensemble technique to fuse the results of different classification networks and the result of each classification network was fed as an input to a simple artificial neural network (ANN) to obtain the final result. Three public datasets, i.e., O...
Development of an automated sub-retinal fluid segmentation technique from optical coherence tomography (OCT) scans is faced with challenges such as no...
Solving the classification problem, unbalanced number of dataset among the classes often causes performance degradation. Especially when some classes ...
Intra-retinal cysts (IRCs) are significant in detecting several ocular and retinal pathologies. Segmentation and quantification of IRCs from optical c...
Deep learning has achieved great success in image classification task when given sufficient labeled training images. However, in fundus image based gl...
We describe and assess convolutional neural network (CNN) models for detection of glaucoma based upon optical coherence tomography (OCT) retinal nerve...
Diabetic retinopathy (DR) is one kind of eye disease that is caused by overtime diabetes. Lots of patients around the world suffered from DR which may...
The objective of this study was to build deep learning models with optical coherence tomography (OCT) images to classify normal and age related macula...
Various ophthalmic procedures critically depend on high-quality images. For instance, efficiency of teleophthalmology, a framework to bring advanced e...
In consideration of the complexity of recording electroencephalography(EEG), some researchers are trying to find new features of emotion recognition. ...
In this work we combine computer vision and a machine learning algorithm, Convolutional Neural Networks (CNNs), to identify obstacles that powered pro...
Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process th...
Existing wheelchair control interfaces, such as sip & puff or screen based gaze-controlled cursors, are challenging for the severely disabled to navig...
A variety of the visual functions of the vertebrate retina rely on the interactions between excitation and inhibition within specialized retinal circu...
PURPOSE: To use supervised machine learning to predict visual function from retinal structure in retinitis pigmentosa (RP) and apply these estimates t...
Proprioception, the ability to sense body position and limb movements in space without visual feedback, is one of the key factors in controlling body ...
Lens-free holographic microscopy (LFHM) provides a cost-effective tool for large field-of-view imaging in various biomedical applications. However, du...
Existing event detection algorithms for eye-movement data almost exclusively rely on thresholding one or more hand-crafted signal features, each compu...
Publications related to artificial intelligence (AI) and machine learning have risen exponentially in the past 5 years in the medical literature, incl...