Pulmonology

Fully 3D Active Surface with Machine Learning for PET Image Segmentation.

In order to tackle three-dimensional tumor volume reconstruction from Positron Emission Tomography (PET) images, most of the existing algorithms rely on the segmentation of independent PET slices. To exploit cross-slice information, typically overlooked in these 2D implementations, I present an algorithm capable of achieving the volume reconstruction directly in 3D, by leveraging an active surface...

Efficacy of bilateral erector spinae block for post-operative analgesia in liver hydatid surgery.

BACKGROUND: Erector spinae plane (ESP) block is a recently described interfacial block, and since 2016, studies have shown that it is a safe technique related to the lower risk of neurovascular and pleural injury comparing to epidural or paravertebral blockade. The application of ESP block in abdominal surgery is relatively limited to case reports and small population studies, which is why we beli...

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develo...

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials.

PURPOSE: To compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose che...

2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The mos...

Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias.

OBJECTIVE: To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD).

Effect of Obesity on Clinical Outcomes of Patients Treated With Cefepime.

As the prevalence of obesity climbs, dosing of antimicrobials, particularly cephalosporins, is becoming a greater challenge for clinicians. Data are ...

Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions.

This work aims to develop a voxel-level dose prediction framework by integrating distance information between PTV and OARs, as well as image informati...

Relationship between Firefighter Physical Fitness and Special Ability Performance: Predictive Research Based on Machine Learning Algorithms.

Firefighters require a high level of physical fitness to meet the demands of their job. The correlations and contributions of individual physical heal...

Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.

Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting ...

Lung Shrinkage: An Additional CT Marker in the Follow-up of Fibrotic Interstitial Lung Disease.

Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease.

BACKGROUND: Deep learning-based radiological image analysis could facilitate use of chest x-rays as triage tests for pulmonary tuberculosis in resourc...

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate predicti...

Predicting antimicrobial resistance using conserved genes.

A growing number of studies are using machine learning models to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence ...

A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans.

We are concerned with the challenge of coronavirus disease (COVID-19) detection in chest X-ray and Computed Tomography (CT) scans, and the classificat...

Development of a prognostic model for mortality in COVID-19 infection using machine learning.

Coronavirus disease 2019 (COVID-19) is a novel disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), whi...

CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation.

BACKGROUND AND OBJECTIVE: An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization o...

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be ...

Extracting Lungs from CT Images via Deep Convolutional Neural Network Based Segmentation and Two-Pass Contour Refinement.

Lung segmentation is a key step of thoracic computed tomography (CT) image processing, and it plays an important role in computer-aided pulmonary dise...

Dyspnea, effort and muscle pain during exercise in lung transplant recipients: an analysis of their association with cardiopulmonary function parameters using machine learning.

BACKGROUND: Despite improvement in lung function, most lung transplant (LTx) recipients show an unexpectedly reduced exercise capacity that could be e...