Pulmonology

Deep learning-enabled system for rapid pneumothorax screening on chest CT.

PURPOSE: Prompt diagnosis and quantitation of pneumothorax impact decisions pertaining to patient management. The purpose of our study was to develop and evaluate the accuracy of a deep learning (DL)-based image classification program for detection of pneumothorax on chest CT.

Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology.

To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy and to assess potential risk factors for post-operative and surgery related mortality. Material consists of 1099 consecutive radical cystectomy (RC) patients operated at 16 different hospitals in Finland 2005-2014. Machine learning methodology was uti...

Commentary: Robotic anatomic pulmonary resection: Cinderella has left the house.

Commentary: Robotic anatomic lung resection: Raising the bar of our expectations in treating lung cancer.

Single patient convolutional neural networks for real-time MR reconstruction: a proof of concept application in lung tumor segmentation for adaptive radiotherapy.

Investigate 3D (spatial and temporal) convolutional neural networks (CNNs) for real-time on-the-fly magnetic resonance imaging (MRI) reconstruction. I...

Deep learning to convert unstructured CT pulmonary angiography reports into structured reports.

BACKGROUND: Structured reports have been shown to improve communication between radiologists and providers. However, some radiologists are concerned a...

An Individualized Prediction Model for Long-term Lung Function Trajectory and Risk of COPD in the General Population.

BACKGROUND: Prediction of future lung function will enable the identification of individuals at high risk of developing COPD, but the trajectory of lu...

Detection of Lung Cancer Lymph Node Metastases from Whole-Slide Histopathologic Images Using a Two-Step Deep Learning Approach.

The application of deep learning for the detection of lymph node metastases on histologic slides has attracted worldwide attention due to its potentia...

Levels of Soluble Urokinase Plasminogen Activator Receptor in Pediatric Lower Respiratory Tract Infections.

Lower respiratory tract infections (LTRIs) are the most common cause of pediatric emergency department visits and are associated with significant mor...

Korean Red Ginseng aqueous extract improves markers of mucociliary clearance by stimulating chloride secretion.

BACKGROUND: Abnormal chloride (Cl) transport has a detrimental impact on mucociliary clearance in both cystic fibrosis (CF) and non-CF chronic rhinosi...

Artificial neural network analysis of the oxygen saturation signal enables accurate diagnostics of sleep apnea.

The severity of obstructive sleep apnea (OSA) is classified using apnea-hypopnea index (AHI). Accurate determination of AHI currently requires manual ...

Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice.

AIM: To test the diagnostic performance of a deep learning-based system for the detection of clinically significant pulmonary nodules/masses on chest ...

2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning.

We have previously developed a robotic ultrasound imaging system for motion monitoring in abdominal radiation therapy. Owing to the slow speed of ultr...

Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm.

PURPOSE: To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging ...

A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk s...

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation.

OBJECTIVE: To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentatio...

Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.

To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT (LDCT) lung cancer screening, we propose a fusion algorithm ...

Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.

Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent t...

Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network.

OBJECTIVES: The aim of this study was to develop a deep convolutional neural network (DCNN) for the prediction of the METAVIR score using B-mode ultra...

Deep learning enables rapid identification of potent DDR1 kinase inhibitors.

We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes s...