Pulmonology

The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for ...

A bilinear convolutional neural network for lung nodules classification on CT images.

PURPOSE: Lung cancer is the most frequent cancer worldwide and is the leading cause of cancer-related deaths. Its early detection and treatment at the stage of a lung nodule improve the prognosis. In this study was proposed a new classification approach named bilinear convolutional neural network (BCNN) for the classification of lung nodules on CT images.

The detection of lung cancer using massive artificial neural network based on soft tissue technique.

BACKGROUND: A proposed computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not notic...

Protein molecular defect detection method based on a neural network algorithm.

Proteins, as the largest macromolecules in the body, are among the most important components of the body and play very vital and important roles. Thes...

Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs.

OBJECTIVES: To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage l...

Automated Lung Segmentation on Chest Computed Tomography Images with Extensive Lung Parenchymal Abnormalities Using a Deep Neural Network.

OBJECTIVE: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest compu...

Synchronous Robot-Assisted Pulmonary and Urologic Resections for Cancer.

Synchronous cancers should be first evaluated at high-volume referral oncological centers. Multidisciplinary evaluation, as the first step of multimod...

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients ef...

Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition.

The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a sc...

The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose.

With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community conc...

Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular i...

Trabeculae microstructure parameters serve as effective predictors for marginal bone loss of dental implant in the mandible.

Marginal bone loss (MBL) is one of the leading causes of dental implant failure. This study aimed to investigate the feasibility of machine learning (...

High neutralizing potency of swine glyco-humanized polyclonal antibodies against SARS-CoV-2.

Perfusion of convalescent plasma (CP) has demonstrated a potential to improve the pneumonia induced by SARS-CoV-2, but procurement and standardization...

Electrochemical SARS-CoV-2 Sensing at Point-of-Care and Artificial Intelligence for Intelligent COVID-19 Management.

To manage the COVID-19 pandemic, development of rapid, selective, sensitive diagnostic systems for early stage β-coronavirus severe acute respiratory ...

Using deep learning to model the biological dose prediction on bulky lung cancer patients of partial stereotactic ablation radiotherapy.

PURPOSE: To develop a biological dose prediction model considering tissue bio-reactions in addition to patient anatomy for achieving a more comprehens...

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments.

Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons ...

Thrombolysis of Pulmonary Emboli via Endobronchial Ultrasound-Guided Transbronchial Needle Injection.

BACKGROUND: Endobronchial ultrasound-guided transbronchial needle injection (EBUS-TBNI) is a novel technique for treating peribronchial targets. The a...

Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

PURPOSE: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning metho...

Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model.

In 2020, Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) Coronavirus, unforeseen pand...

Implementation of convolutional neural network approach for COVID-19 disease detection.

In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classifica...