Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
New oxygenator technologies widened the application of extracorporeal life support significantly in the last decade. Currently the use is still limited within intensive care units. Compared to ventricular assist devices for heart failure, lung replacement technology is lagging behind, not allowing discharge on device. Challenges to achieve a true artificial lung for long term use are discussed in this article.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.thorsurg.2014.09.009 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
TMBquant: an explainable AI-powered caller advancing tumor mutation burden quantification across heterogeneous samples.
Brief Bioinform
August 2025
Department of Respiratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an 710004, China.
Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets.
View Article and Find Full Text PDFExploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.
Crit Care Res Pract
August 2025
Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sepsis remains one of the leading causes of morbidity and mortality worldwide, particularly among critically ill patients in intensive care units (ICUs). Traditional diagnostic approaches, such as the Sequential Organ Failure Assessment (SOFA) and systemic inflammatory response syndrome (SIRS) criteria, often detect sepsis after significant organ dysfunction has occurred, limiting the potential for early intervention. In this study, we reviewed how artificial intelligence (AI)-driven methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can aid physicians.
View Article and Find Full Text PDFSemiopen Ag@Au Plasmonic Nanotube Arrays Synergizing Molecular Confinement and Multimodal SERS for Lung Cancer Breath Diagnostics.
ACS Appl Mater Interfaces
September 2025
College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, P. R. China.
Exhaled breath analysis offers noninvasive, early lung cancer detection via volatile organic compound (VOC) biomarkers, surpassing blood-based methods. Surface-enhanced Raman spectroscopy (SERS) is ideal for this purpose, combining molecular fingerprint specificity with single-molecule sensitivity. However, conventional SERS substrates face a fundamental limitation: while porous materials such as metal-organic frameworks effectively adsorb VOCs through their subnanometer pores (0.
View Article and Find Full Text PDFApplication of computer vision for automated detection of different lesions in pig lungs: An exploratory study.
Prev Vet Med
September 2025
Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna 'Bruno Ubertini' (IZSLER), Via Bianchi 7/9, Brescia 25124, Italy. Electronic address:
Accurate classification of lung lesions at necropsy is crucial for guiding the diagnostic process and ensuring effective management of porcine respiratory diseases. Post-mortem inspection of the lungs during slaughter also provides valuable insights into disease occurrence, offering useful feedback on the efficacy of on-farm prevention and control strategies. However, manual assessment protocols may be impaired by high slaughtering speeds and low inter-rater agreement, which limits continuous data collection and hinders comparability.
View Article and Find Full Text PDFArtificial Intelligence Automation of Echocardiographic Measurements.
J Am Coll Cardiol
August 2025
Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address:
Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.
Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.