Pulmonology

Console and bedside surgeon fused robot-assisted thoracic surgery.

In the last decade, even thoracic surgery has seen an increase in the use of robotic surgical systems, and robot-assisted thoracic surgery (RATS) is considered one of the main issues. While RATS is associated with solo manipulative freedom and high-definition optical systems, several disadvantages, such as the lack of tactile sensation and difficult learning curves for the whole team, have been ra...

Efficient Fabrication of TPU/MXene/Tungsten Disulfide Fibers with Ultra-Fast Response for Human Respiratory Pattern Recognition and Disease Diagnosis via Deep Learning.

Flexible wearable pressure sensors have received increasing attention as the potential application of flexible wearable devices in human health monitoring and artificial intelligence. However, the complex and expensive process of the conductive filler has limited its practical production and application on a large scale to a certain extent. This study presents a kind of piezoresistive sensor by si...

Suspended particulate matter promotes epithelial-to-mesenchymal transition in alveolar epithelial cells via TGF-β1-mediated ROS/IL-8/SMAD3 axis.

Epidemiological evidence presents that dust storms are related to respiratory diseases, such as pulmonary fibrosis (PF). However, the precise underlyi...

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are i...

Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility.

RATIONALE AND OBJECTIVES: The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (...

Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images.

PURPOSE: Deep learning reconstruction (DLR) has been introduced by major vendors, tested for CT examinations of a variety of organs, and compared with...

Silk Fibroin Particles as Carriers in the Development of Hemoglobin-Based Oxygen Carriers.

Oxygen therapeutics have a range of applications in transfusion medicine and disease treatment. Synthetic molecules and all-natural or semi-synthetic ...

Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.

Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs repre...

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, mak...

Deep learning-based automatic detection for pulmonary nodules on chest radiographs: The relationship with background lung condition, nodule characteristics, and location.

PURPOSE: Computer-aided diagnosis (CAD), which assists in the interpretation of chest radiographs, is becoming common. However, few studies have evalu...

Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas fa...

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19.

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed ...

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.

BACKGROUND: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to chan...

Machine learning methods for anomaly classification in wastewater treatment plants.

Modern wastewater treatment plants base their biological processes on advanced control systems which ensure compliance with discharge limits and minim...

Patient Clustering for Vital Organ Failure Using ICD Code With Graph Attention.

OBJECTIVE: Heart failure, respiratory failure and kidney failure are three severe organ failures (OF) that have high mortalities and are most prevalen...

Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians.

Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple ...

Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining.

We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) on computed tomography pulmonary angiography (CTPA...

Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19.

PURPOSE: As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatm...

Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.

AIM: To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features ...

Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.

A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observe...