Background: Advancements in the field of artificial intelligence have lead to the incorporation of automated algorithms in the analysis of medical images and data. Deep learning algorithms have been applied in muscu- loskeletal research to improve the understanding of osteoarthritis and to assist in disease detection and prognosis. The majority of the developed methods examine and process X-ray images and clinical data (age, gender etc.), with a small minority using MRI as inputs. Objective: The current master thesis project aims to investigate the influence of MRI scans on the early detection of knee osteoarthritis through the use of deep learning architectures, and to develop a semi-automatic method for knee region of interest extraction for creating the MRI input of detection algorithms. Methods: The MRI scans used in this project were acquired from the publicly available database of the Os- teoarthritis Initiative. In total 593 dual echo steady state and intermediate-weighted turbo spin-echo sequences were included. The extraction of the knee joint included several processing steps. Initially, a U-Net model was trained on 507 annotated dual echo steady state MRIs for the segmentation of bone and cartilage tissue, which was followed by the registration of the output masks to intermediate-weighted turbo spin-echo sequences in order to create the joint labels for the desired MRI scans. Final step for the region of interest construction included the search of bone coordinates and the creation of the knee joint region of interest. The detection of early osteoarthri- tis progression from knee MRI scans was tested through three different deep learning architectures, a residual network (ResNet), a densely connected convolutional network (DenseNet) and a convolutional variational au- toencoder (CVAE). Furthermore, the probability output of the ResNet and DenseNet as well as the feature vector of the CVAE were coupled with clinical data (age, gender, bone mass index) and used as input to a Logistic Regression Classifier, in order to investigate the influence of osteoarthritis related features to the detection task. The U-Net segmentation method was evaluated using Dice similarity coefficient and Intersection of Union while the detection algorithms using the area under the receiver’s characteristic curve (AUC) and the precision-recall curve (PR-AUC) metrics, with two different input data configurations, only MRI and a combination of MRI and clinical data. Results: The U-Net algorithm for bone and cartilage segmentation showed adequate results, since Dice simi- larity coefficient and Intersection of Union reached mean values higher than 0.99 and 0.88. Regarding the early detection of knee osteoarthritis incidence, ResNet and DenseNet showed similar results, with both methods hav- ing an AUC value ranging from 0.5033 to 0.6269, when only MRI scans were examined. In the case of MRI and clinical data combination, the more complicated deep learning architecture (DenseNet) achieved the highest AUC at 0.6556. The best performing model was CVAE with the largest number of latent space features (1000) achieving an AUC of 0.6699 when combined with clinical data and an AUC of 0.6689 when used alone as input to the logistic regression classifier. All three deep learning algorithms yielded higher performance metrics when clinical data where combined with models’ outputs. Conclusion: The tested deep learning algorithms showed a potential in the challenging task of early detection of knee osteoarthritis through MRI scans, even though they did not reach the same level of performance metrics. The region-of-interest creation had promising results for the implementation of U-Net method for bone tissue labelling.