Latest AI and machine learning research in ophthalmology for healthcare professionals.
We focus on the utility of artificial intelligence (AI) in the management of macular hole (MH). We synthesize 25 studies, comprehensively reporting on each AI model's development strategy, validation, tasks, performance, strengths, and limitations. All models analyzed ophthalmic images, and 5 (20 %) also analyzed clinical features. Study objectives were categorized based on 3 stages of MH care: di...
The electromagnetic spectrum of light from a rainbow is a continuous signal, yet we perceive it vividly in several distinct colour categories. The origins and underlying mechanisms of this phenomenon remain partly unexplained. We investigate categorical colour perception in artificial neural networks (ANNs) using the odd-one-out paradigm. In the first experiment, we compared unimodal vision networ...
Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimagin...
Regulatory approval of the first two therapeutic substances for the management of geographic atrophy (GA) secondary to age-related macular degeneratio...
Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that...
Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer ti...
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many...
The ability to predict how efficiently a person finds an object in the environment is a crucial goal of attention research. Central to this issue are ...
BACKGROUND AND OBJECTIVES: to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique.
PURPOSE: The study explores the evolving landscape of cataract diagnosis, focusing on both traditional methods and innovative technological integratio...
BACKGROUND: Evidence on the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model (LLM), in the ophthalmology question-a...
BACKGROUND/AIMS: This study assesses the proficiency of Generative Pre-trained Transformer (GPT)-4 in answering questions about complex clinical ophth...
PURPOSE: To develop and validate a deep learning model for the segmentation of five retinal biomarkers associated with neovascular age-related macular...
Foundation models represent a paradigm shift in artificial intelligence (AI), evolving from narrow models designed for specific tasks to versatile, ge...
The rapid advancements in generative artificial intelligence are set to significantly influence the medical sector, particularly ophthalmology. Genera...
With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift i...
There is a growing number of publicly available ophthalmic imaging datasets and open-source code for Machine Learning algorithms. This allows ophthalm...
Underwater target detection is of great significance in underwater ecological assessment and resource development. To better protect the environment a...
In the field of computer vision and image recognition, enabling the computer to discern target features while filtering out irrelevant ones poses a ch...
Two-photon high-speed fluorescence calcium imaging stands as a mainstream technique in neuroscience for capturing neural activities with high spatiote...