Pulmonology

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings.

Complexity, costs, and waiting list issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO single-channel recordings from 320 subjects were obtained at patients' homes and were used to automatically obtain statisti...

Functional-guided radiotherapy using knowledge-based planning.

BACKGROUND AND PURPOSE: There are two significant challenges when implementing functional-guided radiotherapy using 4DCT-ventilation imaging: (1) lack of knowledge of realistic patient specific dosimetric goals for functional lung and (2) ensuring consistent plan quality across multiple planners. Knowledge-based planning (KBP) is positioned to address both concerns.

Effective Pneumothorax Detection for Chest X-Ray Images Using Local Binary Pattern and Support Vector Machine.

Automatic image segmentation and feature analysis can assist doctors in the treatment and diagnosis of diseases more accurately. Automatic medical ima...

Novel computer algorithm for cough monitoring based on octonions.

The objective assessment of cough frequency is essential for evaluation of cough and antitussive therapies. Nonetheless, available algorithms for auto...

Semantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks.

Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienc...

A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection.

PURPOSE: The automatic detection of pulmonary nodules using CT scans improves the efficiency of lung cancer diagnosis, and false-positive reduction pl...

A novel feature extraction technique for pulmonary sound analysis based on EMD.

BACKGROUND AND OBJECTIVE: The stethoscope based auscultation technique is a primary diagnostic tool for chest sound analysis. However, the performance...

Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network.

Because influenza is a contagious respiratory illness that seriously threatens public health, accurate real-time prediction of influenza outbreaks may...

Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network.

There is an increasing use of robust analytical algorithms in the prediction of heat stress. The present investigation therefore, was carried out to f...

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clin...

Multichannel lung sound analysis for asthma detection.

BACKGROUND AND OBJECTIVE: Lung sound signals convey valuable information of the lung status. Auscultation is an effective technique to appreciate the ...

A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model.

Preclinical studies of vascular retinal diseases rely on the assessment of developmental dystrophies in the oxygen induced retinopathy rodent model. T...

Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome.

When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the correct labels of some pat...

Agile convolutional neural network for pulmonary nodule classification using CT images.

OBJECTIVE: To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile c...

Development and Validation of a Natural Language Processing Tool to Identify Patients Treated for Pneumonia across VA Emergency Departments.

BACKGROUND: Identifying pneumonia using diagnosis codes alone may be insufficient for research on clinical decision making. Natural language processin...

Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models.

Tuberculosis (TB) is a deadly contagious disease and a serious global health problem. It is curable but due to its lengthy treatment process, a patien...

Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing.

OBJECTIVE: The aim of this research was to develop a swallowing assessment method to help prevent aspiration pneumonia. The method uses simple sensors...

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

RATIONALE AND OBJECTIVES: Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sust...

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer.

BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. In up to 57% of patients, it is diagnosed at an advanced stage and the 5-year ...

Evaluating Report Text Variation and Informativeness: Natural Language Processing of CT Chest Imaging for Pulmonary Embolism.

OBJECTIVE: The aim of this study was to quantify the variability of language in free text reports of pulmonary embolus (PE) studies and to gauge the i...