Radiomics in the assessment of Chronic Rhinosinusitis

<p dir="ltr">Radiomics and Artificial Intelligence (AI) have provided new avenues for developing a deeper understanding about rhinologic disease, above all in chronic rhinosinusitis (CRS). Radiomics is a quantitative approach to medical imaging, based on the extraction of image-based features which can then be analysed by means of machine learning and other AI methodologies. As currently known, radiological features are limited to concepts of “localised” and “diffuse” disease, hindering our ability to further classify, diagnose and treat CRS to improve long term patients’ health outcomes. This thesis focuses on the use of radiomics in rhinology as an emerging field and seeks to utilise an Artificial Intelligence algorithm for defining the radiomic data pertinent to rhinologic disease especially in chronic rhinosinusitis. In addition, this study compares the automated segmentation with manual segmentation methods to optimize the workflow and mitigate the time-consuming and laborious nature of analysis, in order to standardise and speed up the pre-processing pipeline. In the future, these findings can be utilised in big data cluster analysis of CRS for the classification of novel radiological phenotypes and guiding precision medicine for patients.</p>