Ophthalmology

Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data.

PURPOSE: To meet the demands imposed by the continuing growth of the Age-related macular degeneration (AMD) patient population, automation of follow-ups by detecting retinal oedema using deep learning might be a viable approach. However, preparing and labelling data for training is time consuming. In this study, we investigate the feasibility of training a convolutional neural network (CNN) to acc...

Predicting sex from retinal fundus photographs using automated deep learning.

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was perfor...

Classification Criteria for Behçet Disease Uveitis.

PURPOSE: To determine classification criteria for Behçet disease uveitis.

Classification Criteria for Tubercular Uveitis.

PURPOSE: To determine classification criteria for tubercular uveitis.

Classification Criteria for Syphilitic Uveitis.

PURPOSE: To determine classification criteria for syphilitic uveitis.

Classification Criteria for Multiple Sclerosis-Associated Intermediate Uveitis.

PURPOSE: The purpose of this study was to determine classification criteria for multiple sclerosis-associated intermediate uveitis.

Machine Learning Can Predict Anti-VEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVO Associated Macular Edema.

PURPOSE: To assess the potential of machine learning to predict low and high treatment demand in real life in patients with neovascular age-related ma...

Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning.

PURPOSE: The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise...

Speech Vision: An End-to-End Deep Learning-Based Dysarthric Automatic Speech Recognition System.

Dysarthria is a disorder that affects an individual's speech intelligibility due to the paralysis of muscles and organs involved in the articulation p...

Conjunctival Provocation Test With .

Conjunctival provocation test (CPT) is used to demonstrate clinical relevance to a specific allergen. (Bt) is a prevalent allergen in tropical regio...

Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: The objective of this study was to systematically review and meta-analyze the diagnostic accuracy of current machine learnin...

A Zwitterionic-Aromatic Motif-Based ionic skin for highly biocompatible and Glucose-Responsive sensor.

Electronic skins that can sense external stimuli have been of great significance in artificial intelligence and smart wearable devices in recent years...

Understanding inherent image features in CNN-based assessment of diabetic retinopathy.

Diabetic retinopathy (DR) is a leading cause of blindness and affects millions of people throughout the world. Early detection and timely checkups are...

Biology transcends the limits of computation.

Cognition-sensing and responding to the environment-is the unifying principle behind the genetic code, origin of life, evolution, consciousness, artif...

Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening.

IMPORTANCE: A retinopathy of prematurity (ROP) diagnosis currently relies on indirect ophthalmoscopy assessed by experienced ophthalmologists. A deep ...

Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments.

Vision-based autonomous driving through imitation learning mimics the behavior of human drivers by mapping driver view images to driving actions. This...

DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning.

Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale...

Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography.

This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish re...

Effects of subthreshold nanosecond laser therapy in age-related macular degeneration using artificial intelligence (STAR-AI Study).

PURPOSE: To investigate changes in retinal thickness, drusen volume, and visual acuity following subthreshold nanosecond laser (SNL) treatment in pati...

Artificial intelligence-based nomogram for small-incision lenticule extraction.

BACKGROUND: Small-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has bee...