Ophthalmology

Unsupervised learning for large-scale corneal topography clustering.

Machine learning algorithms have recently shown their precision and potential in many different use cases and fields of medicine. Most of the algorithms used are supervised and need a large quantity of labeled data to achieve high accuracy. Also, most applications of machine learning in medicine are attempts to mimic or exceed human diagnostic capabilities but little work has been done to show the...

Eye Explorer: A robotic endoscope holder for eye surgery.

BACKGROUND: Holding endoscopes by hand when performing eye surgery reduces the dexterity of the surgeon.

Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning.

In order to increase the probability of having a successful cataract post-surgery, the customisation of the haptic design of the intraocular lens (IOL...

Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography.

BACKGROUND: It is necessary to consider myopic optic disc tilt as it seriously impacts normal ocular parameters. However, ophthalmologic measurements ...

Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning.

PURPOSE: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net), to segment retinal fluid in diabet...

Deep learning in glaucoma with optical coherence tomography: a review.

Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, par...

Modeling a population of retinal ganglion cells with restricted Boltzmann machines.

The retina is a complex circuit of the central nervous system whose aim is to encode visual stimuli prior the higher order processing performed in the...

Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision.

Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, ...

A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability.

Health data that are publicly available are valuable resources for digital health research. Several public datasets containing ophthalmological imagin...

Deep-learning-based enhanced optic-disc photography.

Optic-disc photography (ODP) has proven to be very useful for optic nerve evaluation in glaucoma. In real clinical practice, however, limited patient ...

Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images.

BACKGROUND/AIMS: Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomog...

Genome-wide association study-based deep learning for survival prediction.

Informative and accurate survival prediction with individualized dynamic risk profiles over time is critical for personalized disease prevention and c...

Comparing biological and artificial vision systems: Network measures of functional connectivity.

Advances in Deep Convolutional Neural Networks (DCNN) provide new opportunities for computational neuroscience to pose novel questions regarding the f...

Detection of Glaucoma Deterioration in the Macular Region with Optical Coherence Tomography: Challenges and Solutions.

PURPOSE: Macular imaging with optical coherence tomography (OCT) measures the most critical retinal ganglion cells (RGCs) in the human eye. The goal o...

An Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population.

Frailty, one of the major public health problems in the elderly, can result from multiple etiologic factors including biological and physical changes ...

AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images.

The scanning laser ophthalmoscopy (SLO) has become an important tool for the determination of peripheral retinal pathology, in recent years. However, ...

[Artificial intelligence for eye care].

Technological developments in ophthalmic imaging and artificial intelligence (AI) create new possibilities for diagnostics in eye care. AI has already...

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning.

Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detect...

Eye Gaze Based 3D Triangulation for Robotic Bionic Eyes.

Three-dimensional (3D) triangulation based on active binocular vision has increasing amounts of applications in computer vision and robotics. An activ...

India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling.

India locked down 1.3 billion people on March 25, 2020, in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, whi...