Source: Virtual Congress 2021 – Digital health interventions in respiratory medicine



Rating:

Share or cite this content

Citations should be made in the following way:
K. Exarchos (Ioannina, Greece), D. Potonos (Ioannina, Greece), A. Aggelopoulou (Ioannina, Greece), A. Sioutkou (Ioannina, Greece), K. Kostikas (Ioannina, Greece). Deep learning algorithm for the classification of spirometries using flow-volume curves: proof of concept study. 3442

Member's Comments

No comment yet.

Related content which might interest you: Machine learning algorithm for the diagnosis of pulmonary embolism: a proof of concept study Source: Virtual Congress 2020 – From diagnosis to management of pulmonary embolism Year: 2020 Introduction of a new approach to interpret pulmonary function tests (PFT) based on Machine learning and Game theory Source: International Congress 2019 – Assessment and management of immune-mediated interstitial lung diseases Year: 2019 Reproducibility of 3He-MRI acquisition assessed by a deep learning approach: ventilation defects in the VaPE-Tox pilot study Source: Virtual Congress 2020 – Imaging-based phenotyping in pulmonary disease Year: 2020 Can recursive partitioning empirically derive a pulmonary function test interpretive algorithm Source: International Congress 2015 – New developments in medical education, web and internet Year: 2015 Explaining predictions of an automated pulmonary function test interpretation algorithm Source: International Congress 2019 – M-health/e-health I Year: 2019 Evaluation of auto-CPAP algorithm using upper-airway simulated model Source: Eur Respir J 2006; 28: Suppl. 50, 410s Year: 2006 Electromagnetic navigation – a new approach in diagnosing peripheral lung lesions: evaluation of the learning curve Source: Eur Respir J 2006; 28: Suppl. 50, 595s Year: 2006 Deep learning automates complete quality control of spirometric manoeuvre Source: Virtual Congress 2020 – Quality improvements in lung function and sleep diagnostics Year: 2020 Which modelling methodology is best? Source: ERS Research Seminar 2017 – The impact of air pollution on respiratory health Year: 2018 Issues for the construction of reference curves for spirometry Source: Eur Respir J 2005; 26: Suppl. 49, 219s Year: 2005 Application of machine learning algorithms to predict loss of asthma control: A post-hoc analysis of INCONTRO study Source: Virtual Congress 2020 – Clinical characteristics and diagnostic tools for phenotyping asthma and COPD Year: 2020 Calculation of "area under curve" from flow-volume curve in idiopatic pulmonary fibrosis and relationship with other parameters Source: Eur Respir J 2006; 28: Suppl. 50, 738s Year: 2006 Application of a three-dimensional kinematic model for prediction of tidal volume in newborns: a pilot study Source: International Congress 2019 – The new-born lung: short- and long-term pathological entities Year: 2019 Prediction model of COPD acute exacerbation with big data by machine learning methods Source: Virtual Congress 2020 – Prediction and management of outcomes in obstructive diseases Year: 2020 Automatic lung sound analysis accuracy: a validation study Source: Virtual Congress 2021 – Digital health interventions in respiratory medicine Year: 2021 Assessing the learning curve for EUS(b)-FNA in comparison with classical ‘blind‘ TBNA Source: Annual Congress 2012 - Ultrasound in pulmonary medicine: from inside and outside Year: 2012 Compression artifact free flow-volume loops used to establish objective measurements in patient effort with spirometry Source: Annual Congress 2011 - Lung and airway function Year: 2011 A computer program for the extrapolation of unfinished forced expiratory volume, time, curves Source: Eur Respir J 2001; 18: Suppl. 33, 13s Year: 2001 Elastic principal graphs for clinical trajectory analysis in COPD: a COPDGene study Source: Virtual Congress 2021 – Deep phenotyping of obstructive diseases for precision medicine Year: 2021 Optimisation and validation of an algorithm for the interpretation of cardio-pulmonary exercise testing Source: Eur Respir J 2002; 20: Suppl. 38, 291s Year: 2002