Pulmonology

Deep Learning-Accelerated Liver Diffusion-Weighted Imaging: Intraindividual Comparison and Additional Phantom Study of Free-Breathing and Respiratory-Triggering Acquisitions.

OBJECTIVES: Deep learning-reconstructed diffusion-weighted imaging (DL-DWI) is an emerging promising time-efficient method for liver evaluation, but analyses regarding different motion compensation strategies are lacking. This study evaluated the qualitative and quantitative features, sensitivity for focal lesion detection, and scan time of free-breathing (FB) DL-DWI and respiratory-triggered (RT)...

Radiomics and artificial intelligence for precision medicine in lung cancer treatment.

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial inte...

Role of the internet of medical things in care for patients with interstitial lung disease.

PURPOSE OF REVIEW: Online technologies play an increasing role in facilitating care for patients with interstitial lung disease (ILD). In this review,...

Deep Learning-Based Diagnosis of Fatal Hypothermia Using Post-Mortem Computed Tomography.

In forensic medicine, fatal hypothermia diagnosis is not always easy because findings are not specific, especially if traumatized. Post-mortem compute...

A deep learning framework for personalised dynamic diagnosis of graft fibrosis after liver transplantation: a retrospective, single Canadian centre, longitudinal study.

BACKGROUND: Recurrent graft fibrosis after liver transplantation can threaten both graft and patient survival. Therefore, early detection of fibrosis ...

Prediction of oxygen uptake kinetics during heavy-intensity cycling exercise by machine learning analysis.

Nonintrusive estimation of oxygen uptake (V̇o) is possible with wearable sensor technology and artificial intelligence. V̇o kinetics have been accurat...

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation.

BACKGROUND: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary...

Artificial intelligence: do we really need it in pulmonary function interpretation?

Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography.

BACKGROUND: High-flow nasal cannula (HNFC) is able to provide ventilation support for patients with hypoxic respiratory failure. Early prediction of H...

Deep learning to estimate lung disease mortality from chest radiographs.

Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnos...

Supervised deep learning with vision transformer predicts delirium using limited lead EEG.

As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians...

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting...

Enhancing Total Optical Throughput of Microscopy with Deep Learning for Intravital Observation.

The significance of performing large-depth dynamic microscopic imaging in vivo for life science research cannot be overstated. However, the optical th...

Outcomes of prostate cancer patients after robot-assisted radical prostatectomy compared with open radical prostatectomy in Korea.

Limited evidence exists regarding the value of robot-assisted radical prostatectomy (RARP) in promoting health outcomes in patients with prostate canc...

Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

BACKGROUND: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy a...

Deep learning classification of capnography waveforms: secondary analysis of the PRODIGY study.

Capnography monitors trigger high priority 'no breath' alarms when CO measurements do not exceed a given threshold over a specified time-period. False...

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial ...

Effects of da Vinci robot versus thoracoscopic surgery on body trauma and lymphocyte subsets in lung cancer patients: A propensity score matching study.

BACKGROUND: To assess the impact of da Vinci robot versus thoracoscopic surgery on body trauma and lymphocyte subsets in lung cancer patients.

Exploring the influence of nasal vestibule structure on nasal obstruction using CFD and Machine Learning method.

Motivated by clinical findings about the nasal vestibule, this study analyzes the aerodynamic characteristics of the nasal vestibule and attempt to de...

Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection.

Toxic gases can be fatal as they damage many living tissues, especially the nervous and respiratory systems. They can cause permanent damage for many ...