University of Twente Student Theses
3D imaging for the prediction of a difficult airway
Loonen, T.G.J. (2017) 3D imaging for the prediction of a difficult airway.
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| Abstract: | All patients undergoing general anaesthesia are evaluated pre-operative for the prediction of a difficult intubation. These intubations can become complicated due to a dificult airway and can result in a "can't intubate can't oxygenate" (CICO) situation with serious consequences, like brain damage and death. The prediction of possible problems allows the anaesthesiologist to change plans or summon help and/or alternative equipment. Poor airway assessment and the failure to change strategy according to the assessment contributes to more airway related complications. The incidence of difficult intubation (1,9%) and complications is relatively small, but with high number of general anaesthetics given (yearly 10,000 at the RadboudUMC), and a possible catastrophic outcome, it is still an important field of research. Difficult airway prediction and airway management is studied for many years and a lot of methods are developed to predict a difficult airway. Unfortunately, all prediction parameters and methods are complicated or little sensitive and little specific [1]. An easy and accurate prediction of a difficult airway can prepare anaesthesiologists to prevent serious complications and stressful situations during general anaesthesia. Therefore, in this study we used 3D imaging techniques to improve the prediction of difficult intubation with two methods. One method is the development of a machine learning prediction model and is further subdivided in two studies: (i) literature study about documentation parameters (Chapter 2) and (ii) Automatic landmarking development for 3D stereophotographs (Chapter 3). The second method and third study (iii) is the use of virtual laryngoscopy (Chapter 4). |
| Item Type: | Essay (Master) |
| Faculty: | TNW: Science and Technology |
| Subject: | 44 medicine, 50 technical science in general |
| Programme: | Technical Medicine MSc (60033) |
| Link to this item: | https://purl.utwente.nl/essays/72427 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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