Ophthalmology

Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.

PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.

Ensemble learning for retinal disease recognition under limited resources.

Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for exe...

G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation.

Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary ...

LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images.

This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distr...

Artificial Intelligence-Assisted Prescription Determination for Orthokeratology Lens Fitting: From Algorithm to Clinical Practice.

OBJECTIVES: To explore the potential of artificial intelligence (AI) to assist prescription determination for orthokeratology (OK) lenses.

A Preliminary Evaluation of the Diagnostic Performance of a Smartphone-Based Machine Learning-Assisted System for Evaluation of Clinical Activity Score in Digital Images of Thyroid-Associated Orbitopathy.

We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (T...

A hybrid framework for glaucoma detection through federated machine learning and deep learning models.

BACKGROUND: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduce...

Machine Teaching Allows for Rapid Development of Automated Systems for Retinal Lesion Detection From Small Image Datasets.

Machine teaching, a machine learning subfield, may allow for rapid development of artificial intelligence systems able to automatically identify emerg...

Visual feedbacks influence short-term learning of torque versus motion profile with robotic guidance among young adults.

Robotic assistance can improve the learning of complex motor skills. However, the assistance designed and used up to now mainly guides motor commands ...

An artificial visual neuron with multiplexed rate and time-to-first-spike coding.

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch b...

OpenAI's Sora in ophthalmology: revolutionary generative AI in eye health.

Recognition of diabetic retinopathy and macular edema using deep learning.

Diabetic retinopathy (DR) and diabetic macular edema (DME) are both serious eye conditions associated with diabetes and if left untreated, and they ca...

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

BACKGROUND: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times hi...

Vision-based estimation of manipulation forces by deep learning of laparoscopic surgical images obtained in a porcine excised kidney experiment.

In robot-assisted surgery, in which haptics should be absent, surgeons experience haptics-like sensations as "pseudo-haptic feedback". As surgeons who...

Clinical applicability of AI-based prognosis prediction for a rare ocular surface disease.

Diabetic retinopathy prediction based on vision transformer and modified capsule network.

Diabetic retinopathy is considered one of the most common diseases that can lead to blindness in the working age, and the chance of developing it incr...

Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy.

To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation o...

Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.

Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesi...

A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagn...

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.

Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite sign...