Prediction of Kellgren-Lawrence Grade of Knee Osteoarthritis by Deep Residual Networks Using MR Image with Segmented Image and Slice Position

Daniele Manfredonia, Daniele Manfredonia, Seiichi Harata, Takuto Sakuma, Francesco Trovò, Shohei Kato

2024

Abstract

This research explores the application of deep learning techniques, specifically employing a residual neural network, to predict Kellgren-Lawrence grade (KLG) in osteoarthritis patients using magnetic resonance images (MRI). Taking advantage of the characteristics of images, the proposed model integrates the MRI slice number and the use of segmented images. Unlike conventional approaches, we adopt a one-to-one image processing strategy, so our model takes each slice individually as input and returns a prediction for each of them to enhance the model’s ability to focus on specific slices and increase the results’ interpretability. Furthermore, results on real-world data corroborate the idea that the segmented image can provide more accurate prediction by allowing our network to focus on the crucial parts of the knee. The empirical results show the model’s promising performance in predicting KLG, demonstrating its potential for accurate and detailed diagnosis of osteoarthritis. This research contributes to advancing studies on the early prediction of osteoarthritis by proposing an effective and interpretable deep-learning framework for osteoarthritis assessment.

Paper Citation

in Harvard Style

Manfredonia D., Harata S., Sakuma T., Trovò F. and Kato S. (2024). Prediction of Kellgren-Lawrence Grade of Knee Osteoarthritis by Deep Residual Networks Using MR Image with Segmented Image and Slice Position. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1300-1307. DOI: 10.5220/0012471200003636

in Bibtex Style

@conference{icaart24,
author={Daniele Manfredonia and Seiichi Harata and Takuto Sakuma and Francesco Trovò and Shohei Kato},
title={Prediction of Kellgren-Lawrence Grade of Knee Osteoarthritis by Deep Residual Networks Using MR Image with Segmented Image and Slice Position},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1300-1307},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012471200003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Prediction of Kellgren-Lawrence Grade of Knee Osteoarthritis by Deep Residual Networks Using MR Image with Segmented Image and Slice Position
SN - 978-989-758-680-4
AU - Manfredonia D.
AU - Harata S.
AU - Sakuma T.
AU - Trovò F.
AU - Kato S.
PY - 2024
SP - 1300
EP - 1307
DO - 10.5220/0012471200003636
PB - SciTePress