Pulmonology

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Sit...

Age-group determination of living individuals using first molar images based on artificial intelligence.

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic...

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) sco...

Novel Machine Learning Can Predict Acute Asthma Exacerbation.

BACKGROUND: Asthma exacerbations result in significant health and economic burden, but are difficult to predict.

Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system.

PURPOSE: To study the effect of different reconstruction parameter settings on the performance of a commercially available deep learning based pulmona...

Breath biopsy of breast cancer using sensor array signals and machine learning analysis.

Breast cancer causes metabolic alteration, and volatile metabolites in the breath of patients may be used to diagnose breast cancer. The objective of ...

Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network.

Accurate identification of the boundaries of organs or abnormal objects (e.g., tumors) in medical images is important in surgical planning and in the ...

Lung cancer histology classification from CT images based on radiomics and deep learning models.

Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fract...

3D multi-scale deep convolutional neural networks for pulmonary nodule detection.

With the rapid development of big data and artificial intelligence technology, computer-aided pulmonary nodule detection based on deep learning has ac...

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data.

Large-scale data sources, remote sensing technologies, and superior computing power have tremendously benefitted to environmental health study. Recent...

Positive Surgical Margins After Robot-Assisted Partial Nephrectomy Predict Long-Term Oncologic Outcomes for Clinically Localized Renal Masses.

For patients with clinically localized renal masses, positive surgical margins (PSMs) after robotic partial nephrectomy (RPN) have been associated wi...

Evaluating the sustainability of big data centers using the analytic network process and fuzzy TOPSIS.

The big data revolution has created data center sustainability problems, whose solutions require the consideration of environmental factors. The purpo...

Prediction of Obstructive Lung Disease from Chest Radiographs via Deep Learning Trained on Pulmonary Function Data.

BACKGROUND: Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is often underdiagnosed.

A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers.

Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities fr...

The diagnosis of tuberculous meningitis in adults and adolescents: protocol for a systematic review and individual patient data meta-analysis to inform a multivariable prediction model.

Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor ...

ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation.

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing...

Utilization of circulating cell-free DNA profiling to guide first-line chemotherapy in advanced lung squamous cell carcinoma.

Platinum-based chemotherapy is one of treatment mainstay for patients with advanced lung squamous cell carcinoma (LUSC) but it is still a "one-size f...

Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images.

This study aimed to use computed tomography (CT) images to assess PD-L1 expression in non-small cell lung cancer (NSCLC) and predict response to immu...

Caffeine mouth rinse enhances performance, fatigue tolerance and reduces muscle activity during moderate-intensity cycling.

We investigated the effects of caffeine mouth rinse on endurance performance, muscle recruitment (i.e., electromyographic activity of the vastus later...

Validation of the Khorana Score for Prediction of Venous Thromboembolism After Robot-Assisted Radical Cystectomy.

The Khorana score (KS) is used to predict the risk of venous thromboembolism (VTE) for cancer patients. We sought to assess the association between K...