Pulmonology

Computerized Modeling of Electrophysiology and Pathoelectrophysiology of the Atria -- How Much Detail is Needed?

This review focuses on the computerized modeling of the electrophysiology of the human atria, emphasizing the simulation of common arrhythmias such as atrial flutter (AFlut) and atrial fibrillation (AFib). Which components of the model are necessary to accurately model arrhythmogenic tissue modifications, including remodeling, cardiomyopathy, and fibrosis, to ensure reliable simulations? The cen...

ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer

Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need. Existing radiomics and deep learning-based predictive models rely primarily on pre-treatment imaging to predict categorical response outcomes, limiting their ability to capture the complex morphological and textural transformations induced by immunotherapy. This study introduces Immu...

Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone

Auscultation is crucial for diagnosing lung diseases. The COVID-19 pandemic has revealed the limitations of traditional, in-person lung sound assess...

Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses

Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability ...

Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method

In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, ...

Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans

Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for i...

Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels

Reconstruction kernels in computed tomography (CT) affect spatial resolution and noise characteristics, introducing systematic variability in quanti...

Advancing Hearing Assessment: An ASR-Based Frequency-Specific Speech Test for Diagnosing Presbycusis

Traditional audiometry often fails to fully characterize the functional impact of hearing loss on speech understanding, particularly supra-threshold...

Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study.

AIM: To assess the predictive performance, risk stratification capabilities, and auxiliary diagnostic utility of radiomics, deep learning, and fusion ...

A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.

Chronic obstructive pulmonary disease (COPD) is considered to be one of the most commonly occurring respiratory disorders and is proliferating at an e...

Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts

The discovery and optimization of materials for specific applications is hampered by the practically infinite number of possible elemental combinati...

Simulating Ethics: Using LLM Debate Panels to Model Deliberation on Medical Dilemmas

This paper introduces ADEPT, a system using Large Language Model (LLM) personas to simulate multi-perspective ethical debates. ADEPT assembles panel...

Sarcopenia and frailty: An in-depth analysis of the pathophysiology and effect on liver transplant candidates.

Cirrhosis represents the end stage of chronic liver disease, significantly reducing life expectancy as it progresses from a compensated to a decompens...

Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.

One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim of this study was to determine whether planning CT...

Development and validation of a CT-based radiomics machine learning model for differentiating immune-related interstitial pneumonia.

INTRODUCTION: Immune checkpoint inhibitor-related interstitial pneumonia (CIP) poses a diagnostic challenge due to its radiographic similarity to othe...

MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support

Timely and personalized treatment decisions are essential across a wide range of healthcare settings where patient responses vary significantly and ...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study ai...

New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance.

Transitional care may play a vital role in the sustainability of Europe's future healthcare system, offering solutions for relocating patient care fro...

First nomogram for predicting interstitial lung disease and pulmonary arterial hypertension in SLE: a machine learning approach.

BACKGROUND: Interstitial lung disease (ILD) and pulmonary arterial hypertension (PAH) are severe, life-threatening complications of systemic lupus ery...

Pixels to Prognosis: Harmonized Multi-Region CT-Radiomics and Foundation-Model Signatures Across Multicentre NSCLC Data

Purpose: To evaluate the impact of harmonization and multi-region CT image feature integration on survival prediction in non-small cell lung cancer ...