Latest AI and machine learning research in infectious disease for healthcare professionals.
Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper, we investigate ways to improve emerging infectious disease forecasting when little pathogen-specific training data are available. Specifically, we explore two sources of information that may be available near the start of an emerging disease outbrea...
BACKGROUND: Nepal offers a distinctive LMIC setting for evaluating AI-health adoption due to its difficult geography, specialist shortage, high case burden needing screening and triage, and emerging digital-health policy reforms. OBJECTIVE: This scoping review aimed to synthesize existing literature on AI applications in medicine and healthcare in Nepal, including clinical use cases, education, im...
Accurate symptom-to-disease classification and clinically-grounded treatment recommendations remain challenging, particularly in heterogeneous patient...
Surgical resection, and its associated bowel preparation, remain the primary treatment for colorectal cancer (CRC), yet the associated effects on post...
Fungal disease and antifungal resistance are growing, historically underrecognized global threats that are difficult to detect and track. In this News...
Tropical theileriosis, caused by the tick-transmitted apicomplexan parasite Theileria annulata, remains a major constraint on cattle production across...
The precise prediction of Antibody-Antigen Interaction (AAI) is a pivotal task for accelerating antibody drug discovery and virtual screening. To addr...
BACKGROUND: Bloodstream infections (BSIs) are a leading cause of morbidity and mortality, yet their clinical heterogeneity continues to challenge effe...
BACKGROUND: Multidrug-resistant (MDR) ESKAPE pathogens, including Enterococcus faecium, S. aureus, Klebsiella pneumoniae, Acinetobacter baumannii, P. ...
Mathematical models play a central role in understanding and forecasting infectious disease dynamics, but parameter inference is often difficult when ...
Type III secretion system effectors (T3SEs) are small bacterial proteins with big biological roles. They act as central molecular mediators of interac...
Carbapenem-resistant Acinetobacter baumannii (CRAB) is one of the most critical public health threats worldwide due to its high infection rates, subst...
The aim of this study was to assess the possibility of deep learning-based object detection models for the early and comprehensive detection of animal...
Polytrauma is commonly defined as multisystem trauma involving at least two body regions with an Abbreviated Injury Scale (AIS) score ≥ 3, characteriz...
PURPOSE: Accurate 3D aortic segmentation in CT images is vital for cardiovascular disease diagnosis, surgical planning, and intraoperative navigation....
Computational methods play a significant role in understanding the interaction between HCMV-encoded miRNAs with their targets. MicroRNAs have a regula...
OBJECTIVE: The COVID-19 pandemic response relied heavily on statistical and machine learning models to predict key outcomes such as case prevalence an...
Microbiome-metabolome interactions are emerging as promising predictors of infectious disease, beyond conventional pathogen detection. Growing evidenc...
The management of acute ischemic stroke has shifted from rigid time-based protocols to imaging-driven, tissue-based reperfusion strategies. Non-contra...
Hepatitis B virus-like particles (HBV-VLPs), characterized by precise biomimetic topological architecture and favorable biocompatibility, have become ...