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

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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: 4.3.2020