Ophthalmology

Deep-learning approach to detect childhood glaucoma based on periocular photograph.

Childhood glaucoma is one of the major causes of blindness in children, however, its diagnosis is of great challenge. The study aimed to demonstrate and evaluate the performance of a deep-learning (DL) model for detecting childhood glaucoma based on periocular photographs. Primary gaze photographs of children diagnosed with glaucoma with appearance features (corneal opacity, corneal enlargement, a...

Interaction of systemic drugs causing ocular toxicity with organic cation transporter: an artificial intelligence prediction.

Chronic disease patients (cancer, arthritis, cardiovascular diseases) undergo long-term systemic drug treatment. Membrane transporters in ocular barriers could falsely recognize these drugs and allow their trafficking into the eye from systemic circulation. Hence, despite their pharmacological activity, these drugs accumulate and cause toxicity at the non-target site, such as the eye. Since around...

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.

Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The ...

Performance of Generative Large Language Models on Ophthalmology Board-Style Questions.

PURPOSE: To investigate the ability of generative artificial intelligence models to answer ophthalmology board-style questions.

Applying Machine Learning to the Visual Inspection of Filled Injectable Drug Products.

With machine learning (ML), we see the potential to better harness the intelligence and decision-making abilities of human inspectors performing manua...

Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.

PURPOSE: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEG...

Canonical circuit computations for computer vision.

Advanced computer vision mechanisms have been inspired by neuroscientific findings. However, with the focus on improving benchmark achievements, techn...

The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach.

PURPOSE: The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, th...

SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation.

Retinal vessel segmentation is an important task in medical image analysis and has a variety of applications in the diagnosis and treatment of retinal...

Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning.

OBJECTIVE: Myasthenia gravis (MG) is an autoimmune disease leading to fatigable muscle weakness. Extra-ocular and bulbar muscles are most commonly aff...

Artificial Intelligence Chatbots in Allergy and Immunology Practice: Where Have We Been and Where Are We Going?

Artificial intelligence (AI) is rapidly becoming a valuable tool in healthcare, providing clinicians with a new AI lens perspective for patient care, ...

Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a gre...

Deep learning for automated detection of neovascular leakage on ultra-widefield fluorescein angiography in diabetic retinopathy.

Diabetic retinopathy is a leading cause of blindness in working-age adults worldwide. Neovascular leakage on fluorescein angiography indicates progres...

Noise reduction performance of a deep learning-based reconstruction in brain computed tomography images acquired with organ-based tube current modulation.

We aimed to evaluate the image quality of brain computed tomography (CT) images reconstructed using deep learning-based reconstruction (DLR) in organ-...

Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning.

Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an i...

Comment on 'Intelligent cataract surgery supervision and evaluation via deep learning'.

Comparing Common Retinal Vessel Caliber Measurement Software with an Automatic Deep Learning System.

PURPOSE: To compare the Retina-based Microvascular Health Assessment System (RMHAS) with Integrative Vessel Analysis (IVAN) for retinal vessel caliber...

Bridging adaptive management and reinforcement learning for more robust decisions.

From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly...

Deep learning in optical coherence tomography: Where are the gaps?

Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of...

FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading.

OBJECTIVE: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and ...