Pneumonia

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

Purpose To investigate whether deep learning models trained on chest radiographs (CXRs) rely on radiographic exposure parameters as shortcut features and to quantify the resulting biases under controlled confounding and natural exposure regimes. Materials and Methods In this retrospective study, CXRs from MIMIC-CXR (January 2011-December 2016), the Medical Imaging and Data Resource Center (MIDRC; ...

How is Bias Learned in Medical Image Analysis Models? An Exploration of the Encoding of Demographic Information in Deep Learning Models Trained to Detect Abnormalities on Chest X-Rays.

Deep learning models achieve strong diagnostic performance in medical imaging, yet often exhibit systematic performance disparities across demographic subgroups. Although prior work has shown that attributes such as age, sex and race are encoded within internal representations, it remains unclear how the structure of these representations contributes to subgroup-level differences in prediction beh...

The reproducibility gap in graph neural network workflows for cell dynamics: A checklist-driven case study.

As part of a Global BioImage Analysts' Society (GloBIAS) initiative, we evaluated the reproducibility of a Graph Neural Network (GNN) study on cell dy...

Detection, localization, and measurement of endotracheal tube positioning on adults' chest X-ray: developing a prediction model.

Accurate placement of the endotracheal tube (ETT) is critical for ensuring optimal care for patients requiring mechanical ventilation and preventing p...

Machine learning based prediction models for first stroke in community primary care: a systematic review and meta-analysis.

BACKGROUND: Stroke is the third leading cause of death and the fourth leading cause of disability globally, particularly in low- and middle-income cou...

Effects of roadside habitat management on epigeic arthropod diversity: a case study from the Nitra-Selenec expressway junction.

Linear transport infrastructure fragments habitats, but its edges can serve as significant refuges for invertebrates. Management of these verges is cr...

Early prediction of severe Omicron pneumonia using a multimodal a.i. model integrating delta CT radiomics and laboratory indicators.

Early identification of patients at risk of severe pneumonia during Omicron SARS-CoV-2 infection is critical for optimizing care and allocating resour...

Patient and staff acceptability of an artificial intelligence software to prioritize chest X-rays in an NHS setting: a qualitative evaluation.

BACKGROUND: Chest X-rays (CXRs) remain the first-line investigation in the lung cancer (LC) diagnostic pathway; however, the time from the original re...

Engineered HSP90-MP65 Bivalent Fusion Antigen: A Novel Vaccine Candidate Against Invasive Candidiasis.

Invasive candidiasis represents a critical global health challenge, causing approximately 6.5 million bloodstream infections annually with mortality r...

Artificial Intelligence-Based Risk Prediction Models for Complications After Tongue Cancer Surgery.

IMPORTANCE: Glossectomy and reconstruction for tongue tumors carries substantial risk of postoperative morbidity, yet current tools offer limited indi...

CellVision: A Deep Learning based Image Analysis Platform to Accelerate Immuno-Plaque Assay Data Processing for Dengue Vaccine Development.

Accurately detecting patterns of interest across a large number of images presents a significant challenge in data analysis for high-throughput analyt...

Guide RNA reprogramming facilitates minimized tracrRNA-dependent off-target and versatile CRISPR/Cas9 engineering.

While innovative, current CRISPR-Cas9 systems face safety concerns and practical hurdles, notably sequence-independent, noncanonical off-targeting. We...

Based on a radiomics-clinical nomogram to predict the therapeutic effect of pulmonary cryptococcosis pneumonia.

We aimed to develop a radiomics-clinical nomogram to predict the therapeutic effect of PC pneumonia. A total of 255 PC pneumonia patients (165 cases w...

LXNet: A lightweight CNN for lung disease classification from Chest X-ray with XAI-based interpretability.

The diagnosis of lung diseases such as pneumonia and tuberculosis remains a major global health challenge, especially in resource-limited regions. Art...

Predicting oxygen levels in microbial habitats using a metagenome-based approach.

Oxygen is a primary driver of the distribution and activity of microbial life. Since oxygen levels are often difficult to measure in situ, one potenti...

AI-Driven Preoperative Chest Radiograph Analysis for Prognostic Stratification in Surgically Resected Pathological Stage 1 Non-Small Cell Lung Cancer.

The purpose of the study is to investigate the potential of artificial intelligence (AI)-driven analysis of preoperative chest radiograph (CXR) for pr...

Identifying Older Adults at Risk for Future Poor Sleep Quality: A Multidimensional Predictive Framework.

OBJECTIVE: To identify multidimensional risk factors associated with poor sleep quality and to develop and temporally validate a machine learning-base...

An uncertainty-aware vision transformer-BiLSTM Bayesian framework for reliable clinical decision support using chest X-rays.

Despite recent progress in deep leaning for medical image analysis, there are still issues of reliability, interpretability, and uncertainty estimatio...

From Flexner to artificial intelligence: one hundred years of Academic Medicine.

As Academic Medicine celebrates a century of contributions to medical education, it is worth examining the questions that drove the inaugural issues o...

Mapping Mpox vaccine hesitancy using an integrated machine learning and structural equation modeling approach.

This study employs an integrated computational approach to investigate Mpox vaccine intention in Bangladesh as Mpox immunisation strategies require a ...