Authors:
Daniele Manfredonia
1
;
2
;
Seiichi Harata
2
;
Takuto Sakuma
2
;
Francesco Trovò
1
and
Shohei Kato
2
Affiliations:
1
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
;
2
Department of Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
Keyword(s):
Knee Osteoarthritis (KOA), Magnetic Resonance Imaging (MRI), Kellgren Lawrence Grade (KLG), Residual Networks, Squeeze-and-Excitation, Segmented Image, Slice Position.
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 re
search contributes to advancing studies on the early prediction of osteoarthritis by proposing an effective and interpretable deep-learning framework for osteoarthritis assessment.
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