2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-MIA.10
Paper Title Towards Deep Learning-Based Sarcopenia Screening with Body Joint Composition Analysis
Authors Yung-Chih Chen, Chang-Gung Memorial Hospital, Taiwan; Jun-Wei Hsieh, National Yang Ming Chiao Tung University, Taiwan; Yao-Hong Yang, Chien-Hung Lee, Chang-Gung Memorial Hospital, Taiwan; Ping-Yang Chen, National Yang Ming Chiao Tung University, Taiwan; Pei-Yi Yu, Arpita Samanta Santa, National Taiwan Ocean University, Taiwan
SessionSS-MIA: Special Session: Deep Learning and Precision Quantitative Imaging for Medical Image Analysis
LocationArea A
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Special Sessions: Deep Learning and Precision Quantitative Imaging for Medical Image Analysis
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Abstract Sarcopenia, a newly recognized geriatric syndrome, now prevalent in the rapidly aging region of Asia, is characterized by the age-related decline of skeletal muscle mass plus relatively low muscle strength and/or physical performance. Doctors screen for sarcopenia by observing patients’ habitual gait features without quantification and the performance of gait disturbances differ in various people. Such a subjective diagnosis has been seen as a problem because diagnostic results may differ among doctors and measurements. This paper proves sarcopenia can be diagnosable from gait patterns. We build a novel automatic deep learning model based on random forest with a modified Long Short-Term Memory (LSTM) to recognize gait features for further clinical analysis. Aligned with the Asian Working Group for Sarcopenia (AWGS) [6] aims, our goal is to facilitate the implementation of standardized sarcopenia diagnosis in clinical practice by providing an automatic gait analysis system. Experimental results demonstrate that our proposed model improves gait recognition performance compared to baseline methods. We believe, the quantitative evaluation provided by our method will assist the clinical diagnosis of sarcopenia and the experimental results on our gait datasets verify the feasibility and effectiveness of the proposed method.