Deep Learning for Knee Osteoarthritis Diagnosis and Progression Prediction from Plain Radiographs and Clinical Data
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
P117, Aapistie 5A, University of Oulu
Topic of the dissertation
Deep Learning for Knee Osteoarthritis Diagnosis and Progression Prediction from Plain Radiographs and Clinical Data
Doctoral candidate
Specialist degree (MSc. equivalent) Aleksei Tiulpin
Faculty and unit
University of Oulu Graduate School, Faculty of Medicine, Research Unit of Medical Imaging, Physics and Technology
Subject of study
Biomedical Engineering
Opponent
Assistant Professor Valentina Pedoia, University of California, San Francisco
Custos
Professor Simo Saarakkala, University of Oulu
Novel AI methods for Knee Osteoarthritis Diagnosis
The present doctoral thesis proposes multiple novel artificial intelligence methods (Deep Learning specifically) designed to tackle clinical imaging-based diagnosis and progression prediction of knee osteoarthritis. The main findings of this study demonstrate that the application of Deep Learning in this realm allows to perform imaging-based diagnostics at a human level, and predict osteoarthritis progression better than it has been done previously.
Last updated: 1.3.2023