Machine learning-based muscle mass estimation using gait parameters in community-dwelling older adults: A cross-sectional study

Fujita, Kosuke; Hiyama, Takahiro; Wada, Kengo; Aihara, Takahiro; Matsumura, Yoshihito; Hamatsuka, Taichi; Yoshinaka, Yasuko; Kimura, Misaka; Kuzuya, Masafumi

doi:10.1016/j.archger.2022.104793

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…Moreover, the same dataset of a previous study using Korean National Fitness Award from 2015 to 2019 indicated that DNN model represented the best performance among physical fitness variables ( 15–17 ). The study explained that including the grip strength variable as a marker of physical fitness improved the prediction of the DNN (Accuracy: 78.4%).…”

Section: Discussionmentioning

confidence: 65%

“…In addition, our deep neural prediction model had predicted that absolute grip strength had a high impact on predicting a possible sarcopenia ( 50 ). Grip strength was a valid and easy tool for early screening of sarcopenia ( 15–17 , 51 ) and was highly related to physical fitness variables ( 15–17 ). Our study also demonstrated that the LIME analysis, as shown in Figure 4 , indicated that the absolute grip strength ranging from 0.39 to 0.52 (original value = 19.71–25.68 kg) was associated with the normal group.…”

Section: Discussionmentioning

confidence: 99%

“…Physical fitness is regarded as a significant indicator of sarcopenia. Nonetheless, previous studies have attempted to predict sarcopenia by measuring only the muscle quantity and blood markers without physical fitness ( 7 , 11 , 15 , 18 , 22 ). Applying a deep-learning model could provide an accurate approach to predicting sarcopenia by analyzing physical fitness and its relationships.…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023

Bae

Seo

Kim

2023

Front. Public Health

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IntroductionPhysical fitness is regarded as a significant indicator of sarcopenia. This study aimed to develop and evaluate a deep-learning model for predicting the decline in physical fitness due to sarcopenia in individuals with potential sarcopenia.MethodsThis study used the 2010–2023 Korean National Physical Fitness Award data. The data comprised exercise- and health-related measurements in Koreans aged >65 years and included body composition and physical fitness variables. Appendicular muscle mass (ASM) was calculated as ASM/height2 to define normal and possible sarcopenia. The deep-learning model was created with EarlyStopping and ModelCheckpoint to prevent overfitting and was evaluated using stratified k-fold cross-validation (k = 5). The model was trained and tested using training data and validation data from each fold. The model’s performance was assessed using a confusion matrix, receiver operating characteristic curve, and area under the curve. The average performance metrics obtained from each cross-validation were determined. For the analysis of feature importance, SHAP, permutation feature importance, and LIME were employed as model-agnostic explanation methods.ResultsThe deep-learning model proved effective in distinguishing from sarcopenia, with an accuracy of 87.55%, precision of 85.57%, recall of 90.34%, and F1 score of 87.89%. Waist circumference (WC, cm), absolute grip strength (kg), and body fat (BF, %) had an influence on the model output. SHAP, LIME, and permutation feature importance analyses revealed that WC and absolute grip strength were the most important variables. WC, figure-of-8 walk, BF, timed up-and-go, and sit-and-reach emerged as key factors for predicting possible sarcopenia.ConclusionThe deep-learning model showed high accuracy and recall with respect to possible sarcopenia prediction. Considering the need for the development of a more detailed and accurate sarcopenia prediction model, the study findings hold promise for enhancing sarcopenia prediction using deep learning.

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Section: Discussionmentioning

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023

Bae

Seo

Kim

2023

Front. Public Health

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“…Thirtytwo gait parameters were created using a three-dimensional skeletal model involving 10-meter comfortable walking. the result of this study showed that Machine learning-based gait analysis is a useful approach to determine the low skeletal muscle mass of community-dwelling older adults (38). Furthermore, Chen et al (1997) investigated how walking speeds influence joint power and the mechanics of muscle output around the hip, knee, and ankle joints.…”

Section: Discussionmentioning

confidence: 90%

“…In a study conducted by Fujita et al (2022) on Sixty-six community-dwelling older adults. Thirtytwo gait parameters were created using a three-dimensional skeletal model involving 10-meter comfortable walking.…”

Section: Discussionmentioning

confidence: 99%

Biomechanical behavior of lower limb joints based on Time-series k- means clustering Algorithm during Gait

Molavian,

Fatahi,

Khezri

2024

Preprint

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Given the inherent essence of gait, there is potential to analyze and categorize this data using artificial intelligence. Aim of this study Investigate the mechanical properties of lower limb articulations while walking through the application of artificial intelligence methodologies. Twenty-eight young individuals were part of a study. They completed a task walking 10 meters on a path. The task was done twice, once at their own speed and once at 100 beats per minute. Data points were categorized using a machine learning technique called time-series k-means clustering. The movements of hip, knee, and ankle joints were compared across different clusters. Statistical analysis was used to find any differences among the groups. Results of the study shows that angular velocity and joint moment influence subject distribution in clusters. Cluster 3 with the smallest population has the highest mechanical power. Mechanical power of ankle and hip joints affects clustering pattern. Second group has few participants with random power allocation among joints. Knee joint is the primary determinant in clustering process. Hip joint has a minor role in power generation and absorption at preferred speed. Knee and ankle joints are crucial in clustering outcomes in all scenarios. The findings indicate notable disparities between the production and absorption stages of walking in situations involving preferred and constant speeds, with the exception of the hip joint.

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Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer

Lin,

Weng,

et al. 2024

Support Care Cancer

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Machine learning-based muscle mass estimation using gait parameters in community-dwelling older adults: A cross-sectional study

Cited by 5 publications

References 24 publications

Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023

Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023

Biomechanical behavior of lower limb joints based on Time-series k- means clustering Algorithm during Gait

Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer

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