Pulmonology

Deep Learning Model for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer.

IMPORTANCE: Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to benefit from immunotherapy.

Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.

PURPOSE: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using high-resolution ...

Speech Detection via Respiratory Inductance Plethysmography, Thoracic Impedance, Accelerometers, and Gyroscopes: A Machine Learning-Informed Comparative Study.

Speech production interferes with the measurement of changes in cardiac vagal activity during acute stress by attenuating the expected drop in heart r...

AI Applications for Thoracic Imaging: Considerations for Best Practice.

Artificial intelligence (AI) technology is rapidly being introduced into thoracic radiology practice. Current representative use cases for AI in thora...

Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer

This study focuses on the classification of cancerous and healthy slices from multimodal lung images. The data used in the research comprises Comput...

Virtual airways heatmaps to optimize point of entry location in lung biopsy planning systems

Purpose: We present a virtual model to optimize point of entry (POE) in lung biopsy planning systems. Our model allows to compute the quality of a b...

A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigatio...

PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion

Traditional remote spirometry lacks the precision required for effective pulmonary monitoring. We present a novel, non-invasive approach using multi...

Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images

Pneumonia is a leading cause of illness and death in children, underscoring the need for early and accurate detection. In this study, we propose a n...

Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays

Chest X-rays play a pivotal role in diagnosing respiratory diseases such as pneumonia, tuberculosis, and COVID-19, which are prevalent and present u...

The Lock Generative Adversarial Network for Medical Waveform Anomaly Detection

Waveform signal analysis is a complex and important task in medical care. For example, mechanical ventilators are critical life-support machines, bu...

Contrastive Representation Learning Helps Cross-institutional Knowledge Transfer: A Study in Pediatric Ventilation Management

Clinical machine learning deployment across institutions faces significant challenges when patient populations and clinical practices differ substan...

Beyond the Lungs: Extending the Field of View in Chest CT with Latent Diffusion Models

The interconnection between the human lungs and other organs, such as the liver and kidneys, is crucial for understanding the underlying risks and e...

Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor

With the advent of the COVID-19 pandemic, ultrasound imaging has emerged as a promising technique for COVID-19 detection, due to its non-invasive na...

Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET

Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current in...

Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with lung metastases being the most common site of distant spread and...

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer

One of the deadliest cancers, lung cancer necessitates an early and precise diagnosis. Because patients have a better chance of recovering, early id...

MedFILIP: Medical Fine-grained Language-Image Pre-training

Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, ...

Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification

Accurately classifying COVID-19 pneumonia in 3D CT scans remains a significant challenge in the field of medical image analysis. Although determinis...

Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment

Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diag...