Pulmonology

Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans.

PURPOSE: Multiview two-dimensional (2D) convolutional neural networks (CNNs) and three-dimensional (3D) CNNs have been successfully used for analyzing volumetric data in many state-of-the-art medical imaging applications. We propose an alternative modular framework that analyzes volumetric data with an approach that is analogous to radiologists' interpretation, and apply the framework to reduce fa...

Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning.

The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory...

Artificial intelligence and machine learning in respiratory medicine.

: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly r...

Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data.

BACKGROUND: Lung cancer is one of the most dangerous malignant tumors, with the fastest-growing morbidity and mortality, especially in the elderly. Wi...

High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

BACKGROUND: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time...

Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an importa...

Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial.

OBJECTIVE: To enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection with the analysis of radio...

Estimating an individual's oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study.

Measurement of oxygen uptake during exercise ([Formula: see text]) is currently non-accessible to most individuals without expensive and invasive equi...

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the perf...

Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center.

INTRODUCTION: Histologic subtypes of malignant pleural mesothelioma are a major prognostic indicator and decision denominator for all therapeutic stra...

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intellig...

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional ne...

Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.

The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema o...

Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.

In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS ...

Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.

Hospital acquired pneumonia (HAP) is the second most common nosocomial infection in the ICU and costs an estimated $3.1 billion annually. The ability ...

Artificial intelligence as the next step towards precision pathology.

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy...

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognos...

Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images.

PURPOSE: In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules...

LungRegNet: An unsupervised deformable image registration method for 4D-CT lung.

PURPOSE: To develop an accurate and fast deformable image registration (DIR) method for four-dimensional computed tomography (4D-CT) lung images. Deep...

Imaging research in fibrotic lung disease; applying deep learning to unsolved problems.

Over the past decade, there has been a groundswell of research interest in computer-based methods for objectively quantifying fibrotic lung disease on...