Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764572
Title: Algorithms for automatic analysis of radiographs of the knee with application in diagnosis and monitoring of osteoarthritis
Author: Thomson, Jessie
ISNI:       0000 0004 7656 7935
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2017
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Abstract:
Osteoarthritis (OA) of the knee is a disease that deteriorates the bones and surrounding soft tissue of the affected joint. Categorisation of the disease into grades of severity is subject to errors of measurement and poor observer agreement. There is an urgent need for automated methods to measure radiographic features and remove, as far as possible, the element of subjectivity in assessment. This project creates a fully automated system to analyse all aspects of the knee in radiographs. The methods evaluate explicit and implicit features of: overall shape, trabecular structure, osteophytes, tibial spines and intercondylar notch, and joint space shape. The project develops the first fully automated osteophyte detection algorithms, improved trabeculae features using raw pixel intensities, and a better analysis of joint space using shape models. This project is the first to combine explicit and implicit features across the whole of the knee, and applies these features to classify radiographs using four main outcomes: current OA, current pain, later onset OA, and later onset pain. The results find a strong current OA classification rate, with an Area Under the ROC Curve (AUC) of 0.904 and weighted kappa of 0.49 (0.48-0.51). The remaining later onset and pain experiments report weaker results; these results suggest that radiographic features in Posterior-Anterior (PA) view radiographs have a weak association with clinical and later onset OA.
Supervisor: Cootes, Timothy ; O'Neill, Terence ; Felson, David Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.764572  DOI: Not available
Keywords: Early Osteoarthritis ; Texture Analysis ; Shape Analysis ; Pain Analysis ; Computer Vision ; Knee Osteoarthritis ; Radiographs
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