Latest AI and machine learning research in infectious disease for healthcare professionals.
Accurate segmentation of lesions plays a critical role in medical image analysis and diagnosis. Traditional segmentation approaches that rely solely on visual features often struggle with the inherent uncertainty in lesion distribution and size. To address these issues, we propose STPNet, a Scale-aware Text Prompt Network that leverages vision-language modeling to enhance medical image segmentat...
Introduction: Tuberculous meningitis (TBM) is a serious brain infection caused by Mycobacterium tuberculosis, characterized by inflammation of the meninges covering the brain and spinal cord. Diagnosis often requires invasive lumbar puncture (LP) and cerebrospinal fluid (CSF) analysis. Objectives: This study aims to classify TBM patients using T1-weighted (T1w) non-contrast Magnetic Resonance Im...
Background. Infectious diseases, particularly COVID-19, continue to be a significant global health issue. Although many countries have reduced or st...
Advances in wearable sensors and artificial intelligence have greatly enhanced the potential of digitised audio biomarkers for disease diagnostics and...
The development of artificial intelligence (AI) including generative large language models (LLMs) and software like ChatGPT is likely to significantly...
Artificial Intelligence (AI) is revolutionizing various fields, including scientific writing, which traditionally relies on human intellectual effort....
Hospital-acquired infections (HAIs) significantly burden global healthcare systems, exacerbated by antibiotic-resistant bacteria. Traditional infectio...
INTRODUCTION: Optimal use of HIV testing resources accelerates progress towards ending HIV as a global threat. In Kenya, current testing practices yie...
Online self-guided interventions appear efficacious for alleviating some mental health concerns. However, among persons who are offered online interve...
BACKGROUND: Diagnosing chronic pulmonary aspergillosis (CPA) and its subtypes is essential for treatment and prognosis. In clinical practice, inexperi...
The practice of pharmacovigilance relies on large databases of individual case safety reports to detect and evaluate potential new causal associatio...
MOTIVATION: Advances in bacterial promoter predictors based on machine learning have greatly improved identification metrics. However, existing models...
In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compar...
Federated continual learning (FCL) offers an emerging pattern to facilitate the applicability of federated learning (FL) in real-world scenarios, wh...
Prior work using Masked Autoencoders (MAEs) typically relies on random patch masking based on the assumption that images have significant redundanci...
Imbalanced data represent a distribution with more frequencies of one class (majority) than the other (minority). This phenomenon occurs across vari...
Federated continual learning (FCL) offers an emerging pattern to facilitate the applicability of federated learning (FL) in real-world scenarios, wh...
Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and ...
Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, ...
As a system of integrated homeostasis, life is susceptible to disruptions by visceral inflammation, which can disturb internal environment equilibri...