TY - GEN
T1 - Detection of Osteoarthritis from Multimodal Hand Data
AU - Andrade Guerreiro, Julian Jorge
AU - Aoki, Yoshimitsu
AU - Saito, Shuntaro
AU - Suzuki, Katsuya
N1 - Funding Information:
*This work was supported by the Keio University Global Research Institute (KGRI) 1Julian Jorge Andrade Guerreiro and Yoshimitsu Aoki are with the Deptartment of Electrical Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Japan julian.guerreiro@keio.jp 2Shuntaro Saito and Katsuya Suzuki are with the Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Osteoarthritis (OA) describes a degenerative joint disorder that is prevalent among older people and typically results in swollen and inflamed joints. The aim of this paper is to develop a method using images, videos and thermal data of 100 patients taken at Keio University Hospital to detect OA in hands. By using hand pose estimation on the video data, joint angles can be calculated and subsequently transformed into feature vectors. For the thermal and RGB images, hand keypoint detectors were trained to identify and crop the appropriate joints within the images. The resulting extracted features are combined and further trained on Support Vector Machines and Convolutional Neural Networks to obtain the final binary classification for each joint. While the proposed method generally shows favorable accuracy and F1-scores on the Proximal (PIP) and Distal Interphalangeal (DIP) joints, the performance on the Metacarpophalangeal (MCP) joints is limited by the low occurrence of affected joints in the dataset. We further compare the different modalities and found that, apart from the combined approach, using video data provides the best results.
AB - Osteoarthritis (OA) describes a degenerative joint disorder that is prevalent among older people and typically results in swollen and inflamed joints. The aim of this paper is to develop a method using images, videos and thermal data of 100 patients taken at Keio University Hospital to detect OA in hands. By using hand pose estimation on the video data, joint angles can be calculated and subsequently transformed into feature vectors. For the thermal and RGB images, hand keypoint detectors were trained to identify and crop the appropriate joints within the images. The resulting extracted features are combined and further trained on Support Vector Machines and Convolutional Neural Networks to obtain the final binary classification for each joint. While the proposed method generally shows favorable accuracy and F1-scores on the Proximal (PIP) and Distal Interphalangeal (DIP) joints, the performance on the Metacarpophalangeal (MCP) joints is limited by the low occurrence of affected joints in the dataset. We further compare the different modalities and found that, apart from the combined approach, using video data provides the best results.
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U2 - 10.1109/EMBC48229.2022.9871560
DO - 10.1109/EMBC48229.2022.9871560
M3 - Conference contribution
C2 - 36086624
AN - SCOPUS:85138128687
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3607
EP - 3610
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -