Ophthalmology

Using natural language processing for identification of herpes zoster ophthalmicus cases to support population-based study.

IMPORTANCE: Diagnosis codes are inadequate for accurately identifying herpes zoster (HZ) ophthalmicus (HZO). There is significant lack of population-based studies on HZO due to the high expense of manual review of medical records.

Comparison of Machine-Learning Classification Models for Glaucoma Management.

This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured u...

Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

PURPOSE: Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variabl...

Active Perception With Dynamic Vision Sensors. Minimum Saccades With Optimum Recognition.

Vision processing with dynamic vision sensors (DVSs) is becoming increasingly popular. This type of a bio-inspired vision sensor does not record stati...

Tracing cell lineages in videos of lens-free microscopy.

In vitro experiments with cultured cells are essential for studying their growth and migration pattern and thus, for gaining a better understanding of...

Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine Learning.

Eye disease is a major health problem among the elderly people. Cataract and corneal arcus are the major abnormalities that exist in the anterior segm...

Deep learning in ophthalmology: a review.

Deep learning is an emerging technology with numerous potential applications in Ophthalmology. Deep learning tools have been applied to different diag...

Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images.

PURPOSE: Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, e...

Association between serum tumor necrosis factor-alpha level and the efficacy of infliximab for refractory pouchitis after restorative proctocolectomy in patients with ulcerative colitis.

OBJECTIVES: Although the aetiology of pouchitis after restorative proctocolectomy in ulcerative colitis (UC) remains unknown, infliximab (IFX) is ofte...

Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods.

Diabetic retinopathy (DR) is an eye abnormality caused by long-term diabetes and it is the most common cause of blindness before the age of 50. Microa...

Structure-Preserving Guided Retinal Image Filtering and Its Application for Optic Disk Analysis.

Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degenera...

Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm - support vector regression model.

BACKGROUND AND OBJECTIVES: The refractive index of hemoglobin plays important role in hematology due to its strong correlation with the pathophysiolog...

Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning.

We present a robust deep learning framework for the automatic localisation of cone photoreceptor cells in Adaptive Optics Scanning Light Ophthalmoscop...

Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

PURPOSE: To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algor...

Robot-Guided Neuronavigated Repetitive Transcranial Magnetic Stimulation (rTMS) in Central Neuropathic Pain.

OBJECTIVES: To confirm and extend previous results involving repetitive transcranial magnetic stimulation (rTMS) aimed at alleviating refractory centr...

Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks.

This paper investigates the automatic monitoring of tool usage during a surgery, with potential applications in report generation, surgical training a...

Sharpening of Hierarchical Visual Feature Representations of Blurred Images.

The robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up, recurrent, and to...

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients wit...

Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach.

The optical properties of blood play crucial roles in medical diagnostics and treatment, and in the design of new medical devices. Haemoglobin is a vi...

Retinal blood vessel segmentation using fully convolutional network with transfer learning.

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the ac...