Pulmonology

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.

Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized v...

Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies.

Non-alcoholic fatty liver disease (NAFLD) affects about 24% of the world's population. Progression of early stages of NAFLD can lead to the more advanced form non-alcoholic steatohepatitis (NASH), and ultimately to cirrhosis or liver cancer. The current gold standard for diagnosis and assessment of NAFLD/NASH is liver biopsy followed by microscopic analysis by a pathologist. The Kleiner score is f...

Generative adversarial network based data augmentation for CNN based detection of Covid-19.

Covid-19 has been a global concern since 2019, crippling the world economy and health. Biological diagnostic tools have since been developed to identi...

Artificial intelligence in lung cancer: current applications and perspectives.

Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the develo...

Deep learning-based framework for slide-based histopathological image analysis.

Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images ...

A Fissure-Aided Registration Approach for Automatic Pulmonary Lobe Segmentation Using Deep Learning.

The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challe...

Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade.

Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligan...

Multi-scale, domain knowledge-guided attention + random forest: a two-stage deep learning-based multi-scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting...

Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study.

PURPOSE: To compare the image properties and pulmonary nodule volumetric accuracies among deep learning-based reconstruction (DLR), filtered back proj...

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomar...

A novel deep learning-based method for COVID-19 pneumonia detection from CT images.

BACKGROUND: The sensitivity of RT-PCR in diagnosing COVID-19 is only 60-70%, and chest CT plays an indispensable role in the auxiliary diagnosis of CO...

Diaphragmatic paralysis post COVID-19 treated with robot-assisted plication reinforced with acellular dermal matrix: a report of two cases.

Coronavirus disease 2019 (COVID-19) continues to be a disease of global importance, with an increasing array of sequelae attributed to infection by th...

The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.

BACKGROUND: Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagno...

ognitive utcomes in the ragmatic nvestigation of optimaxygen argets (CO-PILOT) trial: protocol and statistical analysis plan.

INTRODUCTION: Long-term cognitive impairment is one of the most common complications of critical illness among survivors who receive mechanical ventil...

Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training.

BACKGROUND: Currently, the diagnosis of invasive pulmonary aspergillosis (IPA) mainly depends on the integration of clinical, radiological and microbi...

A routine de-airing method for total endoscopic robot-assisted mitral valve repair.

Intraoperative water testing is essential for assessing mitral valve morphology and degree of residual mitral regurgitation during mitral valve repair...

Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology.

Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that...

Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital.

Sepsis is an inflammation caused by the body's systemic response to an infection. The infection could be a result of many diseases, such as pneumonia,...

CX-DaGAN: Domain Adaptation for Pneumonia Diagnosis on a Small Chest X-Ray Dataset.

Recent advances in deep learning led to several algorithms for the accurate diagnosis of pneumonia from chest X-rays. However, these models require la...

A CNN-transformer fusion network for COVID-19 CXR image classification.

The global health crisis due to the fast spread of coronavirus disease (Covid-19) has caused great danger to all aspects of healthcare, economy, and o...