Ophthalmology

Can Artificial Intelligence Make Screening Faster, More Accurate, and More Accessible?

Diabetic retinopathy, glaucoma, and age-related macular degeneration are leading causes of vision loss and blindness worldwide. They tend to be asymptomatic in the early phase of disease and therefore require active screening programs to identify the patients requiring referral and treatment. Deep learning-based artificial intelligence technology has recently become a major topic in the field of o...

Data on Vulnerability Detection in Android.

The data in this article have been collaborated from mainly four sources- Google Playstore, Wandoujia (third party app store market), AMD and Androzoo. These data include ~85,000 APKs (Android Package Kit), both malicious and benign from these data sources. Static and dynamic features are extracted from these APK files, and then supervised machines learning algorithms are employed for malware dete...

Artificial Intelligence-Based Grading Quality of Bovine Blastocyst Digital Images: Direct Capture with Juxtaposed Lenses of Smartphone Camera and Stereomicroscope Ocular Lens.

In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to th...

Asynchronous Multiplex Communication Channels in 2-D Neural Network With Fluctuating Characteristics.

Neurons behave like transistors, but have fluctuating characteristics. In this paper, we show that several asynchronous multiplex communication channe...

Biomarkers and polymorphisms in pancreatic neuroendocrine tumors treated with sunitinib.

Several circulating biomarkers and single nucleotide polymorphisms (SNPs) have been correlated with efficacy and tolerability to antiangiogenic agents...

Endophthalmitis following cataract surgery and intracameral antibiotic: Moxifloxacin resistant .

PURPOSE: To describe an immunosuppressed patient who developed acute-onset postoperative endophthalmitis caused by a moxifloxacin-resistant strain of ...

Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage.

There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric...

Weakly Supervised Lesion Detection From Fundus Images.

Early diagnosis and continuous monitoring of patients suffering from eye diseases have been major concerns in the computer-aided detection techniques....

Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques.

BACKGROUND AND OBJECTIVE: Glaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is imp...

Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for...

Introduction to Machine Learning for Ophthalmologists.

New diagnostic and imaging techniques generate such an incredible amount of data that it is often a challenge to extract all information that could be...

A deep learning approach to automatic detection of early glaucoma from visual fields.

PURPOSE: To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CN...

A deep learning model for the detection of both advanced and early glaucoma using fundus photography.

PURPOSE: To build a deep learning model to diagnose glaucoma using fundus photography.

DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs.

PURPOSE: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale pred...

Deep transfer learning-based hologram classification for molecular diagnostics.

Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditio...

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning.

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identifica...

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery.

Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate to...

An Interpretable and Expandable Deep Learning Diagnostic System for Multiple Ocular Diseases: Qualitative Study.

BACKGROUND: Although artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a spe...

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.

The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON) in fundus photographs was evaluated. A large database of fu...

Tacrolimus Suppositories in Therapy-Resistant Ulcerative Proctitis.

BACKGROUND: Ulcerative proctitis may often be managed with topical salicylates or steroids alone, but in some patients, symptoms are persistent and se...