INTRODUCTION: Anaemia during pregnancy is a widespread health burden globally, especially in low- and middle-income countries, posing a serious risk to both maternal and neonatal health. The primary challenge is that anaemia is frequently undetected ...
BACKGROUND: The advent of generative artificial intelligence has led to the emergence of multiple vision large language models (VLLMs). This study aimed to evaluate the capabilities of commonly available VLLMs, such as OpenAI's GPT-4V and Google's Ge...
PURPOSE: To examine the influence of artificial intelligence (AI) on physicians' judgments regarding the presence and severity of glaucoma on fundus photographs in an online simulation system.
Virological plaque assay is the major method of detecting and quantifying infectious viruses in research and diagnostic samples. Furthermore, viral plaque phenotypes contain information about the life cycle and spreading mechanism of the virus formin...
PURPOSE: To develop a deep learning method for vessel segmentation in fundus images, measure retinal vessels, and study the connection between retinal vascular features and systemic indicators in diabetic patients.
OBJECTIVES: Convolutional neural networks (CNNs) have demonstrated remarkable success in orthodontics. This study aimed to evaluate the accuracy and precision of several prominent CNN models for evaluating the facial attractiveness in Chinese orthodo...
AIMS: To evaluate the acceptability, applicability, and cost-utility of AI-powered portable fundus cameras for diabetic retinopathy (DR) screening in Hong Kong, providing a viable alternative screening solution for resource-limited areas.
OBJECTIVES: To evaluate the technical assets of a new imaging device that, wifi linked to a AI based algorithm, automatically grades in vivo the exfoliating process of the skin, taking dandruff as model.
PURPOSE: To compare two artificial intelligence (AI) models, residual neural networks ResNet-50 and ResNet-101, for screening thyroid eye disease (TED) using frontal face photographs, and to test these models under clinical conditions.
BACKGROUND: Due to high patient demand, increasing numbers of non-dermatologists are performing skin assessments and carrying out laser interventions in medical spas, leading to inferior outcomes and higher complications. A machine learning tool that...