Pulmonology

A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.

Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-lear...

Artificially-generated consolidations and balanced augmentation increase performance of U-net for lung parenchyma segmentation on MR images.

PURPOSE: To improve automated lung segmentation on 2D lung MR images using balanced augmentation and artificially-generated consolidations for training of a convolutional neural network (CNN).

AI-Guided Computing Insights into a Thermostat Monitoring Neonatal Intensive Care Unit (NICU).

In any healthcare setting, it is important to monitor and control airflow and ventilation with a thermostat. Computational fluid dynamics (CFD) simula...

Deep learning for diagnosis of malign pleural effusion on computed tomography images.

BACKGROUND: The pleura is a serous membrane that surrounds the lungs. The visceral surface secretes fluid into the serous cavity and the parietal surf...

Outcome-Supervised Deep Learning on Pathologic Whole Slide Images for Survival Prediction of Immunotherapy in Patients with Non-Small Cell Lung Cancer.

Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in patients with non-small cell lung cancer (NSCLC), approxim...

Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis: Effects of moving health technologies in global health.

Computer Aided Detection software based on Artificial Intelligence (AI-CAD), combined with chest X-rays have recently been promoted as an easy fix for...

A discontinuity-preserving regularization for deep learning-based cardiac image registration.

. Sliding motion may occur between organs in anatomical regions due to respiratory motion and heart beating. This issue is often neglected in previous...

Application of an artificial intelligence ensemble for detection of important secondary findings on lung ventilation and perfusion SPECT-CT.

RATIONALE: Single-photon-emission-computerized-tomography/computed-tomography(SPECT/CT) is commonly used for pulmonary disease. Scant work has been do...

Implementation of digital home monitoring and management of respiratory disease.

PURPOSE OF REVIEW: Digital respiratory monitoring interventions (e.g. smart inhalers and digital spirometers) can improve clinical outcomes and/or org...

Novel radiomic features versus deep learning: differentiating brain metastases from pathological lung cancer types in small datasets.

OBJECTIVE: Accurate diagnosis and early treatment are crucial for survival in patients with brain metastases. This study aims to expand the capability...

Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning.

Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and...

Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.

IMPORTANCE: Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic ...

Effectiveness of to reduce inflammation markers and the length of stay of patients with community-acquired pneumonia: An open-label clinical trial.

Examination of the interleukin 6 (IL-6) and procalcitonin levels, and neutrophil-lymphocyte ratio (NLR) might could help to diagnosis and predict the ...

Quantitative assessment of myocardial fibrosis by digital image analysis: An adjunctive tool for pathologist "ground truth".

AIMS: Myocardial fibrosis (MF) is a common pathological process in a wide range of cardiovascular diseases. Its quantity has diagnostic and prognostic...

Can Machine Learning Be Better than Biased Readers?

Training machine learning (ML) models in medical imaging requires large amounts of labeled data. To minimize labeling workload, it is common to divid...

Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment F-FDG PET/CT Using Deep Learning.

RATIONALE AND OBJECTIVES: To develop an end-to-end deep learning (DL) model for non-invasively predicting non-small cell lung cancer (NSCLC) pathologi...

Interstitial Lung Abnormalities at CT in the Korean National Lung Cancer Screening Program: Prevalence and Deep Learning-based Texture Analysis.

Background Interstitial lung abnormalities (ILAs) are associated with worse clinical outcomes, but ILA with lung cancer screening CT has not been quan...

Deep learning on graphs for multi-omics classification of COPD.

Network approaches have successfully been used to help reveal complex mechanisms of diseases including Chronic Obstructive Pulmonary Disease (COPD). H...

Classification of pulmonary sounds through deep learning for the diagnosis of interstitial lung diseases secondary to connective tissue diseases.

Early diagnosis of interstitial lung diseases secondary to connective tissue diseases is critical for the treatment and survival of patients. The symp...

RIMD: A novel method for clinical prediction.

Electronic health records (EHR) are sparse, noisy, and private, with variable vital measurements and stay lengths. Deep learning models are the curren...