Pulmonology

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements.

A deep learning-based drug repurposing screening and validation for anti-SARS-CoV-2 compounds by targeting the cell entry mechanism.

The recent outbreak of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a severe threat to the global public health and economy, however, effective drugs to treat COVID-19 are still lacking. Here, we employ a deep learning-based drug repositioning strategy to systematically screen potential anti-SARS-CoV-2 drug candidates that tar...

Antibacterial and anti-inflammatory activities of and extracts on staphylococcal mastitis animal model.

Inappropriate and prolonged administration of antibiotics in mastitis could cause resistance and herbal treatment might could be one alternative treat...

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; ho...

Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis.

Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments...

Short-term outcomes of robot-assisted versus video-assisted thoracoscopic surgery for non-small cell lung cancer patients with neoadjuvant immunochemotherapy: a single-center retrospective study.

BACKGROUND: Neoadjuvant immunochemotherapy has been increasingly applied to treat non-small cell lung cancer (NSCLC). However, the comparison between ...

A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy.

. Respiration introduces a constant source of irregular motion that poses a significant challenge for the precise irradiation of thoracic and abdomina...

Propensity-matched analysis of robotic versus sternotomy approaches for mitral valve replacement.

To compare early and medium-term outcomes between robotic and sternotomy approaches for mitral valve replacement (MVR). Clinical data of 1393 cases wh...

Computed Tomography-Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease-Associated Interstitial Lung Disease.

OBJECTIVES: This study aimed to develop a computed tomography (CT)-based deep learning model for assessing the severity of patients with connective ti...

Robot-assisted thoracoscopic right upper lobectomy with displaced B and absence of minor fissure: a case report.

INTRODUCTION: B downward-shifting is a rare bronchial anomaly characterized by abnormal pulmonary arteries associated with downward displacement of B ...

A Survey on Artificial Intelligence in Pulmonary Imaging.

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities o...

Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data.

Mitochondrial toxicity is a significant concern in the drug discovery process, as compounds that disrupt the function of these organelles can lead to ...

Experimental drugs in clinical trials for COPD: artificial intelligence via machine learning approach to predict the successful advance from early-stage development to approval.

INTRODUCTION: Therapeutic advances in drug therapy of chronic obstructive pulmonary disease (COPD) really effective in suppressing the pathological pr...

AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD.

BACKGROUND: Liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated...

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

BACKGROUND: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in ...

Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit.

BACKGROUND: Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and...

Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image.

BACKGROUND: The main application of [18F] FDG-PET ( FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and C...

Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography.

OBJECTIVES: This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification...

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Da...

COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.

The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially ches...