Pulmonology

Machine learning identifies 10 feature miRNAs for lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a common type of malignancy. The mechanism behind its tumor progression is not clear yet. The aim of this study is to use machine learning to identify the feature miRNAs, which can be reliably used as biomarkers for diagnosis LUSC. We downloaded microRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus...

Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD.

Accurate detection and quantification of hepatic fibrosis remain essential for assessing the severity of non-alcoholic fatty liver disease (NAFLD) and its response to therapy in clinical practice and research studies. Our aim was to develop an integrated artificial intelligence-based automated tool to detect and quantify hepatic fibrosis and assess its architectural pattern in NAFLD liver biopsies...

Scoring pleurisy in slaughtered pigs using convolutional neural networks.

Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short l...

Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.

OBJECTIVES: To compare the diagnostic accuracy of texture analysis (TA)-derived parameters combined with machine learning (ML) of non-contrast-enhance...

Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Severa...

Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care.

BACKGROUND: Interstitial lung disease requires frequent re-examination, which directly causes excessive cumulative radiation exposure. To date, AI has...

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.

In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized...

Comparison of Xpert MTB/RIF (G4) and Xpert Ultra, including trace readouts, for the diagnosis of pulmonary tuberculosis in a TB and HIV endemic setting.

BACKGROUND: There are limited data about Xpert-Ultra performance in different settings, in HIV-infected persons, in those with a history of previous T...

Meta-Analysis Based on Nonconvex Regularization.

The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. Howe...

Automatic mandibular canal detection using a deep convolutional neural network.

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging...

Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early...

Extension of Coronavirus Disease 2019 on Chest CT and Implications for Chest Radiographic Interpretation.

PURPOSE: To study the extent of pulmonary involvement in coronavirus 19 (COVID-19) with quantitative CT and to assess the impact of disease burden on ...

Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning.

The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result i...

Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.

OBJECTIVES: We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary...

Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disea...

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis...

Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.

PURPOSE: Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatur...

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic ...

Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non-small cell lung cancer patients at a lung cancer center in China.

BACKGROUND: IBM Watson for Oncology (WFO) provides physicians with evidence-based treatment options. This study was designed to explore the concordanc...

Deep learning approaches for sleep disorder prediction in an asthma cohort.

OBJECTIVE: Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower p...