COPD

A deep-learning based biomarker of systemic cellular senescence burden to predict mortality and health outcomes

Introduction: The accumulation of senescent cells is a recognized hallmark of biological aging and is associated with the onset of multiple chronic medical conditions. Senescent cells exhibit a distinct secretory profile, known as the senescence-associated secretory phenotype (SASP), which can propagate cellular senescence to neighboring and distant tissues. Measuring SASP factors in blood serves ...

Beyond AI Psychosis and Sycophancy: Structural Drift as a System-Level Safety Failure

Background: Conversational AI safety systems are primarily evaluated using message-level content monitoring, which assesses inputs and outputs in isolation. This message-by-message approach can miss interaction-level risks that emerge over extended conversations, including patterns discussed in reports of "AI psychosis." Critically, by the time users express overt psychosis-spectrum content, oppor...

Computational Fluid Particle Dynamics-Informed Machine Learning Prototype for a User-Centered Smart Inhaler Enabling Uniform Drug Delivery to Small Airways

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles ...

HybridNet-XR: Efficient Teacher-Free Self-Supervised Learning for Autonomous Medical Diagnostic Systems in Resource-Constrained Environments.

Deep learning model classification on large datasets is often limited in countries with restricted computational resources. While transfer learning ca...

RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation

Radiology report annotation is essential for clinical NLP, yet manual labeling is slow and costly. We present RadAnnotate, an LLM-based framework that...

A comprehensive multimodal dataset and benchmark for ulcerative colitis scoring in endoscopy

Ulcerative colitis (UC) is a chronic mucosal inflammatory condition that places patients at increased risk of colorectal cancer. Colonoscopic surveill...

Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseases

Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk predict...

Can Machine Learning Algorithms use Contextual Factors to Detect Unwarranted Clinical Variation from Electronic Health Record Encounter Data during the Treatment of Children Diagnosed with Acute Viral Pharyngitis

Rationale, Aims and Objectives: Unwarranted clinical variation (UCV) in patient care often arises from contextual factors and contributes to increased...

Deep learning-derived quantitative interstitial abnormalities in early rheumatoid arthritis and healthy controls: A multicenter, prospective cross-sectional study

Objective: Quantitative computed tomography (QCT) can automatically quantify parenchymal abnormalities on chest CT imaging using deep learning. We lev...

When Fusion Helps and When It Breaks: View-Aligned Robustness in Same-Source Financial Imaging

We study same-source multi-view learning and adversarial robustness for next-day direction prediction with financial image representations. On Shangha...

CytoCrowd: A Multi-Annotator Benchmark Dataset for Cytology Image Analysis

High-quality annotated datasets are crucial for advancing machine learning in medical image analysis. However, a critical gap exists: most datasets ei...

Gold Exploration using Representations from a Multispectral Autoencoder

Satellite imagery is employed for large-scale prospectivity mapping due to the high cost and typically limited availability of on-site mineral explora...

Reducing the labeling burden in time-series mapping using Common Ground: a semi-automated approach to tracking changes in land cover and species over time

Reliable classification of Earth Observation data depends on consistent, up-to-date reference labels. However, collecting new labelled data at each ti...

Stroke Lesions as a Rosetta Stone for Language Model Interpretability

Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which model components are truly necessary for language func...

Accurate Stride Length Prediction from Proximal IMU Sensors Using a Compact Linear Model

Objective: Accurate stride length measurement is essential for assessing functional mobility, yet gold-standard methods remain confined to laboratory ...

LungCRCT: Causal Representation based Lung CT Processing for Lung Cancer Treatment

Due to silence in early stages, lung cancer has been one of the most leading causes of mortality in cancer patients world-wide. Moreover, major sympto...

Detecting Cardiac Amyloidosis in Italian Cardiology Reports: Structured Variable Extraction versus Direct Free-Text Analysis

Background: Early and accurate identification of cardiac amyloidosis improves patient outcomes, yet relevant evidence is frequently hidden in free-tex...

Trend-Adjusted Time Series Models with an Application to Gold Price Forecasting

Time series data play a critical role in various fields, including finance, healthcare, marketing, and engineering. A wide range of techniques (from c...

Creating a scalable CT yield metric for pulmonary embolisms in the emergency department using an open-source large language model

BackgroundCT scans are the gold-standard diagnostic test for pulmonary embolisms (PE). Despite stable PE prevalence, CT use is rising in emergency dep...

Utilizing artificial intelligence and medical experts to identify predictors for common diagnoses in dyspneic adults: A cross-sectional study of consecutive emergency department patients from Southern Sweden.

OBJECTIVE: Half of all adult emergency department (ED) visits with a complaint of dyspnea involve acute heart failure (AHF), exacerbation of chronic o...