Ophthalmology

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

OBJECTIVE: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications.

Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.

PURPOSE: To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with OCT and OCT angiography (OCTA) in eyes with macular edema from various diagnoses.

A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor.

A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death and the finding of a body. Despite various methods ...

A look at the emerging trends of large language models in ophthalmology.

PURPOSE OF REVIEW: As the surge in large language models (LLMs) and generative artificial intelligence (AI) applications in ophthalmology continue to ...

Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration.

BACKGROUND AND OBJECTIVE: For the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate cam...

From pre-training to fine-tuning: An in-depth analysis of Large Language Models in the biomedical domain.

In this study, we delve into the adaptation and effectiveness of Transformer-based, pre-trained Large Language Models (LLMs) within the biomedical dom...

Application of Artificial Intelligence in the Diagnosis, Follow-Up and Prediction of Treatment of Ophthalmic Diseases.

PURPOSE: To describe the application of artificial intelligence (AI) in ophthalmic diseases and its possible future directions.

Assessment of autostereoscopic perception using artificial intelligence-enhanced face tracking technology.

PURPOSE: Stereopsis, the ability of humans to perceive depth through distinct visual stimuli in each eye, is foundational to autostereoscopic technolo...

Amaurosis after Inferior Alveolar Nerve Block Injection in a Seven-Year-Old Girl: A Case Report and Review of the Literature.

A seven-year-old girl was referred for the treatment of her primary teeth. An inferior alveolar nerve block was administered using 1 mL of 2% lidocain...

USCT-UNet: Rethinking the Semantic Gap in U-Net Network From U-Shaped Skip Connections With Multichannel Fusion Transformer.

Medical image segmentation is a crucial component of computer-aided clinical diagnosis, with state-of-the-art models often being variants of U-Net. De...

Artificial intelligence-based analysis of retinal fluid volume dynamics in neovascular age-related macular degeneration and association with vision and atrophy.

BACKGROUND/OBJECTIVES: To characterise morphological changes in neovascular age-related macular degeneration (nAMD) during anti-angiogenic therapy and...

Inspiration from Visual Ecology for Advancing Multifunctional Robotic Vision Systems: Bio-inspired Electronic Eyes and Neuromorphic Image Sensors.

In robotics, particularly for autonomous navigation and human-robot collaboration, the significance of unconventional imaging techniques and efficient...

A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss.

Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images ...

Classification of Internal and External Distractions in an Educational VR Environment Using Multimodal Features.

Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroom. However, distraction among participants in a VR...

Neuroscientific insights about computer vision models: a concise review.

The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch...

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment dec...

Effect of childhood atropine treatment on adult choroidal thickness using sequential deep learning-enabled segmentation.

PURPOSE: To describe choroidal thickness measurements using a sequential deep learning segmentation in adults who received childhood atropine treatmen...

Accuracy of large language models in answering ophthalmology board-style questions: A meta-analysis.

PURPOSE: To evaluate the accuracy of large language models (LLMs) in answering ophthalmology board-style questions.

The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review.

With the ongoing revolution of artificial intelligence (AI) in medicine, the impact of AI in radiology is more pronounced than ever. An increasing num...

A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.

INTRODUCTION: Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a significant challenge in disease management due to i...