Pneumonia

Broad-spectrum coronavirus inhibitors discovered by modeling viral fusion dynamics.

Development of oral, broad-spectrum therapeutics targeting SARS-CoV-2, its variants, and related coronaviruses could curb the spread of COVID-19 and avert future pandemics. We created a novel computational discovery pipeline that employed molecular dynamics simulation (MDS), artificial intelligence (AI)-based docking predictions, and medicinal chemistry to design viral entry inhibitors that target...

YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation.

OBJECTIVE: Early and accurate detection of COVID-19 and pneumonia through medical imaging is critical for effective patient management. This study aims to develop a robust framework that integrates synthetic image augmentation with advanced deep learning (DL) models to address dataset imbalance, improve diagnostic accuracy, and enhance trust in artificial intelligence (AI)-driven diagnoses through...

Artificial intelligence in vaccine research and development: an umbrella review.

BACKGROUND: The rapid development of COVID-19 vaccines highlighted the transformative potential of artificial intelligence (AI) in modern vaccinology,...

IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective.

COVID-19 has not only changed the way people live but has also altered the way all organizations operate. The most effective precautionary measure aga...

Sarcopenia prediction model based on machine learning and SHAP values for community-based older adults with cardiovascular disease in China.

BACKGROUND: Sarcopenia (SP), is recognized as a complication of cardiovascular disease (CVD), but few relevant diagnostic models have been developed. ...

Modeling protective meningococcal antibody responses and factors influencing antibody persistence following vaccination with MenAfriVac using machine learning.

Meningococcal meningitis poses a significant public health burden in the meningitis belt region of sub-Saharan Africa. The introduction of the meningo...

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

BACKGROUND: The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe ac...

FedPneu: Federated Learning for Pneumonia Detection across Multiclient Cross-Silo Healthcare Datasets.

BACKGROUND: Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the ...

Innovative Silicosis and Pneumonia Classification: Leveraging Graph Transformer Post-hoc Modeling and Ensemble Techniques

This paper presents a comprehensive study on the classification and detection of Silicosis-related lung inflammation. Our main contributions include...

Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines

The COVID-19 vaccine development, manufacturing, transportation, and administration proved an extreme logistics operation of global magnitude. Globa...

VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control

While diffusion models show extraordinary talents in text-to-image generation, they may still fail to generate highly aesthetic images. More specifi...

Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data

This study aims to explore the automatic classification method of pneumonia X-ray images based on VGG19 deep convolutional neural network, and evalu...

Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index

This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations o...

Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG

Deep learning has advanced medical image classification, but interpretability challenges hinder its clinical adoption. This study enhances interpret...

BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models

Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to their accessibility and aff...

DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imagi...

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation

Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localiz...

ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports

The increasing adoption of AI-generated radiology reports necessitates robust methods for detecting hallucinations--false or unfounded statements th...

Future Aspects in Human Action Recognition: Exploring Emerging Techniques and Ethical Influences

Visual-based human action recognition can be found in various application fields, e.g., surveillance systems, sports analytics, medical assistive te...

The IBEX Imaging Knowledge-Base: A Community Resource Enabling Adoption and Development of Immunofluoresence Imaging Methods

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunoflu...