Machine-Learning Based Assessment of Cystic Fibrosis

Cystic fibrosis is a genetic disease that affects over 162,428 people worldwide. Currently, assessing cystic fibrosis from medical images requires a trained expert to manually annotate regions in the patient's lungs to determine the stage and severity of the disease. This process takes a substantial amount of time and effort to achieve an accurate assessment.

Recent advancements in machine learning and deep learning have been effective in solving classification, decision-making, identification, and segmentation problems in various disciplines. In medical research, these techniques have been used to perform image analyses that aid in organ identification, tissue classification, and lesion segmentation, which reduces the time required for physicians to analyze medical images. However, these techniques have yet to be widely applied in the assessment of cystic fibrosis.

This thesis describes an automated framework employed to assess the severity and extent of cystic fibrosis. The framework comprises three analysis stages: airways analysis, texture analysis, and lung lesions detection, that are utilized to extract cystic fibrosis features from CT scans, and which are used to assess the severity and extent of cystic fibrosis. The framework achieved an accuracy of 86.96\% in the staging process. The main contribution of this work is the development of a data-driven methodology used to design a quantitative cystic fibrosis staging and grading model.