Magnetic resonance imaging-based biomarkers of knee osteoarthritis progression via deep learning

Thesis event information

Date and time of the thesis defence

Place of the thesis defence

Auditorium F202, Kontinkangas campus

Topic of the dissertation

Magnetic resonance imaging-based biomarkers of knee osteoarthritis progression via deep learning

Doctoral candidate

Master of Engineering and Technology Egor Panfilov

Faculty and unit

University of Oulu Graduate School, Faculty of Medicine, Research Unit of Health Sciences and Technology

Subject of study

Medical Physics and Technology

Opponent

Associate Professor Cem M. Deniz, New York University

Custos

Professor Simo Saarakkala, University of Oulu

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Predicting knee osteoarthritis development and progression from magnetic resonance images using AI

Knee osteoarthritis is one of the leading causes of disability worldwide, especially in developed countries. It leads to structural joint degeneration, pain, stiffness, and reduced mobility. However, not everyone with this disease experiences it the same way. Predicting who is at higher risk of progression — and how quickly the condition will worsen — remains a major challenge in medicine today.

Magnetic Resonance Imaging (MRI) provides detailed images of joint tissues, but comprehensive manual analysis of this data is time-consuming and requires expert knowledge. This thesis aimed to develop artificial intelligence (AI)-based methods that can automatically extract imaging biomarkers from MRI scans and to evaluate their potential for predicting knee osteoarthritis progression.

The first part of the thesis focused on developing a deep learning method that automatically identifies articular cartilage tissue from MRI images and quantifies its health status. These quantitative biomarkers offer a more precise way to track how the disease affects the knee joint over time. The method was validated using several MRI protocols and performed comparably to expert-guided semi-automatic software tools.

The second part of the thesis addressed a broader question: how well can progression of knee osteoarthritis be predicted using whole-knee MRI scans — not just cartilage tissue — and which MRI protocols are the most informative? Using AI, imaging biomarkers were learned directly from the multi-protocol MRI data. The predictive performance of AI models was analyzed across multiple time horizons, highlighting the contribution of individual MRI protocols.

To conclude, this thesis demonstrates how AI enables efficient, large-scale MRI analysis in osteoarthritis research and supports the identification of patients at risk of disease progression. All the tools developed are openly shared to support future studies and clinical translation.
Last updated: 4.7.2025