Ophthalmology

Latest AI and machine learning research in ophthalmology for healthcare professionals.

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Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning.

BACKGROUND: Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data ...

Deep Learning-Enabled Vasculometry Depicts Phased Lesion Patterns in High Myopia Progression.

PURPOSE: To investigate the potential phases in myopic retinal vascular alterations for further eluc...

Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review.

Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporativ...

TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers.

Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face ...

Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.

BACKGROUND: Objective and standardized evaluation of surgical skills in robot-assisted surgery (RAS)...

A Multi-Group Multi-Stream attribute Attention network for fine-grained zero-shot learning.

Fine-grained visual categorization in zero-shot setting is a challenging problem in the computer vis...

STC-UNet: renal tumor segmentation based on enhanced feature extraction at different network levels.

Renal tumors are one of the common diseases of urology, and precise segmentation of these tumors pla...

A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images.

The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal In...

Bifurcation detection in intravascular optical coherence tomography using vision transformer based deep learning.

. Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a signifi...

[OCT biomarkers in diabetic maculopathy and artificial intelligence].

Diabetes mellitus is a chronic disease the microvascular complications of which include diabetic ret...

Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation.

Adaptation is a universal aspect of neural systems that changes circuit computations to match prevai...

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review.

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particula...

Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications.

There is a significant demand for multiplexed fluorescence sensing and detection across a range of a...

Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives.

The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosin...

Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia.

The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant...

Screening for diabetic retinopathy with artificial intelligence: a real world evaluation.

AIM: Periodic screening for diabetic retinopathy (DR) is effective for preventing blindness. Artific...

Establishment of a corneal ulcer prognostic model based on machine learning.

Corneal infection is a major public health concern worldwide and the most common cause of unilateral...

Selection of pre-trained weights for transfer learning in automated cytomegalovirus retinitis classification.

Cytomegalovirus retinitis (CMVR) is a significant cause of vision loss. Regular screening is crucial...

A hybrid model for the detection of retinal disorders using artificial intelligence techniques.

The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to ...

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