Robust Repeatable Respiratory Monitoring with EIT
Lead Research Organisation:
King's College London
Department Name: Asthma Allergy and Lung Biology
Abstract
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| Description | A new system has been developed for monitoring lungs using electrical impedance and it is undergoing testing. Progress has been made on reconstruction algorithms including automatic segmentation and meshing based on lung images. The feasibility of using EIT data for automatic control of ventilation has been demonstrated in simulations. |
| Exploitation Route | We are moving forward to testing the systems on volunteers and patients |
| Sectors | Electronics,Healthcare |
| Description | The software improvements associated with the EIT model have been helpful in furthering the Feld more generally, and some of the clinical insights gained around respiratory physiology proved helpful in terms if the ventilatory strategies employed during the COVID Pandemic. |
| First Year Of Impact | 2018 |
| Sector | Digital/Communication/Information Technologies (including Software),Healthcare |
| Impact Types | Societal |
| Description | Partnership with University of Gottingen |
| Organisation | University of Göttingen |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Developing relationship on advanced respiratory monitoring with the University of Gottingen |
| Collaborator Contribution | Developing relationship in the field of advanced monitoring of respiratory function in critically ill patients with severe respiratory failure, with our group represented by Dr Luigi Camporota, and our partners by Prof Miachael Quintel and Prof Luciano Gattinoni |
| Impact | Ongoing work on animal model of healthy lungs ventilated with different mechanical power. EIT used to evaluate, lung water, lung homogeneity and distribution of ventilation using commercial devices but off line analysis of data using dedicated research algorithms and EIDORS models. This collaboration is now testing real life clinical conditions (ARDS) in patients undergoing mechanical ventilation and ECMO. |
| Start Year | 2016 |
| Title | R3M |
| Description | The overall project is structured in four different phases comprising an initial engineering and mathematical phase (during which EIT electrodes are designed and built, and a software algorithm is developed) of a later phase, with clinical involvement: a clinical bench to bedside testing of the device and software algorithm developed. The project is now approaching the end of the initial engineering phase. However, KCL is contributing to this initial phase by acquiring electrical impedance tomography images - using a commercially available system and analysis tool- and pairing them to anonymised chest CT images, obtained in a protocolised fashion in tightly defined severe ARDS patient population. This allows better definition of the geometrical parameters necessary to build the new software and better define pattern of electrode placement." |
| Type Of Technology | New/Improved Technique/Technology |
| Year Produced | 2015 |
| Impact | Enabling ongoing project work |