Ophthalmology

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

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Rapid evaluation of Curcuma origin and quality based on E-eye, flash GC e-nose, and FT-NIR combined with machine learning technologies.

Curcuma, a key ingredient in curry and a popular health supplement, has been subject to adulteration...

Hybrid deep learning framework for diabetic retinopathy classification with optimized attention AlexNet.

Diabetic retinopathy (DR) is a chronic condition associated with diabetes that can lead to vision im...

Evaluating multimodal ChatGPT for emergency decision-making of ocular trauma cases.

PURPOSE: This study aimed to evaluate the potential of ChatGPT in diagnosing ocular trauma cases in ...

Sensor-driven digital motion correction of robotically-aligned optical coherence tomography retinal volumes.

Optical coherence tomography (OCT) has revolutionized diagnostics in retinal ophthalmology. Traditio...

Demographic bias of expert-level vision-language foundation models in medical imaging.

Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging a...

High precision banana variety identification using vision transformer based feature extraction and support vector machine.

Bananas, renowned for their delightful flavor, exceptional nutritional value, and digestibility, are...

Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.

Continuous high-resolution imaging of the disease-mediating blood stages of the human malaria parasi...

Microscope-Assisted Hypertensive Retinopathy Diagnosis Using Deep Learning Models.

The retina is the most crucial part of the human eye, and it can be affected due to hypertension. Ho...

A hybrid bioelectronic retina-probe interface for object recognition.

Retina converts light stimuli into spike firings, encoding abundant visual information critical for ...

Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in...

Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, ...

High-level visual processing in the lateral geniculate nucleus revealed using goal-driven deep learning.

BACKGROUND: The Lateral Geniculate Nucleus (LGN) is an essential contributor to high-level visual pr...

Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method.

Glaucoma is the leading cause of irreversible vision impairment, emphasizing the critical need for e...

PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data.

: Glaucoma (GL) classification is crucial for early diagnosis and treatment, yet relying solely on s...

Artificial intelligence to enhance the diagnosis of ocular surface squamous neoplasia.

To provide an artificial intelligence (AI) method using in vivo confocal microscopy (IVCM) to differ...

Deep learning-based normative database of anterior chamber dimensions for angle closure assessment: the Singapore Chinese Eye Study.

BACKGROUND/ AIMS: The lack of context for anterior segment optical coherence tomography (ASOCT) meas...

Improving traceability and quality control in the red-meat industry through computer vision-driven physical meat feature tracking.

Current traceability systems rely heavily on external markers which can be altered or tampered with....

A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging.

Stroke is a leading cause of death and disability in developed countries. We validated an AI-based p...

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