Pulmonology

Vaccine Therapy in Non-Small Cell Lung Cancer.

Immunotherapy using immune checkpoint modulators has revolutionized the oncology field, emerging as a new standard of care for multiple indications, including non-small cell lung cancer (NSCLC). However, prognosis for patients with lung cancer is still poor. Although immunotherapy is highly effective in some cases, not all patients experience significant or durable responses, and further strategie...

An improved CNN-based architecture for automatic lung nodule classification.

Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient's chance of surviving is mostly done in two phases: screening through CT scan imaging modality and, more importantly the medical expert's reading of the scan, which is a time-consuming task and is vulnerable to errors...

Incidentalomas in chest CT.

Advances in imaging technology have dramatically increased the resolution of CT and improved detection of disease; these advances also have led to an ...

Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction.

PURPOSE: Quantitative analysis of emphysema volume is affected by the radiation dose and the CT reconstruction technique. We aim to evaluate the influ...

Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease.

BACKGROUND: Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Ch...

Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.

OBJECTIVE: In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was t...

Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning.

This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U-Net) and multilevel boundary sensing residual U-s...

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.

OBJECTIVE: Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstru...

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset.

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applicatio...

A novel deep learning prognostic system improves survival predictions for stage III non-small cell lung cancer.

BACKGROUND: Accurate prognostic prediction plays a crucial role in the clinical setting. However, the TNM staging system fails to provide satisfactory...

MHA-CoroCapsule: Multi-Head Attention Routing-Based Capsule Network for COVID-19 Chest X-Ray Image Classification.

The outbreak of COVID-19 threatens the lives and property safety of countless people and brings a tremendous pressure to health care systems worldwide...

Deep Learning Based Joint PET Image Reconstruction and Motion Estimation.

Respiratory motion is one of the main sources of motion artifacts in positron emission tomography (PET) imaging. The emission image and patient motion...

Cloud-based neuro-fuzzy hydro-climatic model for water quality assessment under uncertainty and sensitivity.

River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). How...

Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?

BACKGROUND: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of ...

Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients.

The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research com...

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning.

In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative mea...

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease.

Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the g...

Comparison of radiologist versus natural language processing-based image annotations for deep learning system for tuberculosis screening on chest radiographs.

Although natural language processing (NLP) can rapidly extract disease labels from radiology reports to create datasets for deep learning models, this...

Very low-volume interval training improves nonalcoholic fatty liver disease fibrosis score and cardiometabolic health in adults with obesity and metabolic syndrome.

Non-alcoholic fatty liver disease (NAFLD) and cardiometabolic disorders are highly prevalent in obese individuals. Physical exercise is an important e...

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.

Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less attention to the unique aspects of pediatric imaging. The...