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Fundus Oculi

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CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images.

Medical & biological engineering & computing
Accurate segmentation of the optic disk (OD) and optic cup (OC) regions of the optic nerve head is a critical step in glaucoma diagnosis. Existing architectures based on convolutional neural networks (CNNs) still suffer from insufficient global infor...

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence.

Biomedical physics & engineering express
Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts ea...

A Novel Management Challenge in Age-Related Macular Degeneration: Artificial Intelligence and Expert Prediction of Geographic Atrophy.

Ophthalmology. Retina
PURPOSE: The progression of geographic atrophy (GA) secondary to age-related macular degeneration is highly variable among individuals. Prediction of the progression is critical to identify patients who will benefit most from the first treatments cur...

Machine learning model for age-related macular degeneration based on heavy metals: The National Health and Nutrition Examination Survey 2005 to 2008.

Scientific reports
Age-related macular degeneration (AMD) is the leading cause of blindness in older people in developed countries. It has been suggested that heavy metal exposure may be associated with the development of AMD, but most studies have focused on the effec...

A generalised computer vision model for improved glaucoma screening using fundus images.

Eye (London, England)
IMPORTANCE: Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. A novel, computer vision-based model for glaucoma screening using fundus images co...

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.

Scientific reports
Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets hav...

Performance of automated machine learning in detecting fundus diseases based on ophthalmologic B-scan ultrasound images.

BMJ open ophthalmology
AIM: To evaluate the efficacy of automated machine learning (AutoML) models in detecting fundus diseases using ocular B-scan ultrasound images.

Prediction of Visual Acuity After Cataract Surgery by Deep Learning Methods Using Clinical Information and Color Fundus Photography.

Current eye research
PURPOSE: To examine the performance of deep-learning models that predicts the visual acuity after cataract surgery using preoperative clinical information and color fundus photography (CFP).

An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing.

Scientific reports
Innovation is currently driving enhanced performance and productivity across various fields through process automation. However, identifying intricate details in images can often pose challenges due to morphological variations or specific conditions....