Pulmonology

Optimization: Molecular Communication Networks for Viral Disease Analysis Using Deep Leaning Autoencoder.

Developing new treatments for emerging infectious diseases in infectious and noninfectious diseases has attracted a particular attention. The emergence of viral diseases is expected to accelerate; these data indicate the need for a proactive approach to develop widely active family specific and cross family therapies for future disease outbreaks. Viral disease such as pneumonia, severe acute respi...

Is artificial intelligence ready to solve mechanical ventilation? Computer says blow.

Artificial intelligence (AI) has the potential to identify treatable phenotypes, optimise ventilation strategies, and provide clinical decision support for patients who require mechanical ventilation. Gallifant and colleagues performed a systematic review to identify studies using AI to solve a diverse range of clinical problems in the ventilated patient. They identify 95 studies, the majority of ...

Re: Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital.

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans.

Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turn...

Artificial intelligence in functional imaging of the lung.

Artificial intelligence (AI) is transforming the way we perform advanced imaging. From high-resolution image reconstruction to predicting functional r...

Native BK virus nephropathy in lung transplant: a case report and literature review.

Classically described in renal allografts, BK virus nephropathy is increasingly recognized in native kidneys of other non-renal solid organ transplant...

Site-Specific Regulated Memristors via Electron-Beam-Induced Functionalization of HfO.

Emerging nonvolatile resistive switching, also known as the memristor, works with a distinct concept that relies mainly on the change in the compositi...

Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review.

BACKGROUND: Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions....

Deep learning for anatomical interpretation of video bronchoscopy images.

Anesthesiologists commonly use video bronchoscopy to facilitate intubation or confirm the location of the endotracheal tube; however, depth and orient...

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare.

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, s...

Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model.

Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human dis...

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 co...

Prospective machine learning CT quantitative evaluation of idiopathic pulmonary fibrosis in patients undergoing anti-fibrotic treatment using low- and ultra-low-dose CT.

AIM: To compare the machine learning computed tomography (CT) quantification tool, Computer-Aided Lung Informatics for Pathology Evaluation and Rating...

Deep multi-instance transfer learning for pneumothorax classification in chest X-ray images.

PURPOSE: Pneumothorax is a life-threatening emergency that requires immediate treatment. Frontal-view chest X-ray images are typically used for pneumo...

Improving robustness of a deep learning-based lung-nodule classification model of CT images with respect to image noise.

. Robustness is an important aspect to consider, when developing methods for medical image analysis. This study investigated robustness properties of ...

Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study.

BACKGROUND: Diagnosis of pneumonia is critical in managing patients with febrile neutropenia (FN), however, chest X-ray (CXR) has limited performance ...

Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network.

BACKGROUND: The inferior alveolar nerve (IAN) innervates and regulates the sensation of the mandibular teeth and lower lip. The position of the IAN sh...

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography.

OBJECTIVES: To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect sim...

Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis.

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings....

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT.

Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (C...