1,721,171 research outputs found
Artificial Neural Networks (ANN) in the Assessment of Respiratory Mechanics
No full textThe aim of this thesis was to test the capability of Artificial Neural Networks (ANN) to estimate respiratory mechanics during mechanical ventilation (MV). ANNs are universal function approximators and can extract information from complex signals. We evaluated, in an animal model of acute lung injury, whether ANN can assess respiratory system resistance (RRS) and compliance (CRS) using the tracings of pressure at airways opening (PAW), inspiratory flow (V’) and tidal volume, during an end-inspiratory hold maneuver (EIHM). We concluded that ANN can estimate CRS and RRS during an EIHM. We also concluded that the use of tracings obtained by non-biological models in the learning process has the potential of substituting biological recordings. We investigated whether ANN can extract CRS using tracings of PAW and V’, without any intervention of an inspiratory hold maneuver during continuous MV. We concluded that CRS can be estimated by ANN during volume control MV, without the need to stop inspiratory flow. We tested whether ANN, fed by inspiratory PAW and V’, are able to measure static total positive end-expiratory pressure (PEEPtot,stat) during ongoing MV. In an animal model we generated dynamic pulmonary hyperinflation by shortening expiratory time. Different levels of external PEEP (PEEPAPP) were applied. Results showed that ANN can estimate PEEPtot,stat reliably, without any influence from the level of PEEPAPP. We finally compared the robustness of ANN and multi-linear fitting (MLF) methods in extracting CRS when facing signals corrupted by perturbations. We observed that during the application of random noise, ANN and MLF maintain a stable performance, although in these conditions MLF may show better results. ANN have more stable performance and yield a more robust estimation of CRS than MLF in conditions of transient sensor disconnection. We consider ANN to be an interesting technique for the assessment of respiratory mechanics
Acute Respiratory Distress Syndrome (ARDS) : Pathophysiological Insights and Lung Imaging
No full textAcute respiratory distress syndrome (ARDS) is in the center of the scientific debate both for its complex pathophysiology and for the discussion about the remedies that could contribute to its healing. The intricate interplay of different body systems that characterizes ARDS is mirrored by two main research threads, one centered on the pathophysiological mechanisms of the disease and the other on the new approaches to lung imaging. In this Special Issue of the Journal of Clinical Medicine are presented studies using imaging technologies based on electrical impedance tomography, synchrotron radiation computed tomography and intravital probe-based confocal laser endomicroscopy. The studies on the pathophysiological mechanisms pertain to the evaluation of the biomarkers of the disease and the platelet disfunction during extracorporeal membrane oxygenation. These contributions witness the intensity of ARDS research as many of the key problems of the disease are only in part resolved
Impiego di Reti Neurali Artificiali nel Calcolo della Resistenza e della Compliance del Sistema Respiratorio: Studio su Modello
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