Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital

Cristian, C; Soto, B. Garcia-Zapirain; Lozano, Christian Carballo; Zúñiga, Clemente

doi:10.3390/ijerph16183275

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…This could potentially limit the model’s capacity to encapsulate the complete variation of sarcopenia across disparate populations. A significant interpretability issue is raised by Castillo et al [ 106 ]. They highlight that SVM models are frequently seen as “black box” models, which pose a challenge in interpreting the path leading to the model’s predictions.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

Turimov Mustapoevich,

Kim

2023

Healthcare

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This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

Turimov Mustapoevich,

Kim

2023

Healthcare

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“…On average, muscle mass loss is approximately 5 kg every 10 years after the age of 40 [ 9 ]. Therefore, the prevalence of sarcopenia varies between 1% and 50% in the elderly [ 10 ] and it is estimated that 5% to 13% of people aged 60–70 years suffer from sarcopenia and 11% to 50% in older people aged 80 years or more [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Does an Association among Sarcopenia and Metabolic Risk Factors Exist in People Older Than 65 Years? A Systematic Review and Meta-Analysis of Observational Studies

Carcelén-Fraile

Aibar-Almazán

Afanador-Restrepo³

et al. 2023

Life

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Sarcopenia is defined as the generalized and progressive loss of skeletal muscle strength and mass that may be affected by metabolic factors, although this relationship has been poorly studied. The aim of this review and meta-analysis was to analyze the relationship among the different metabolic risk factors and sarcopenia in people older than 65 years. Following the PRISMA 2020 guide, we searched for articles that studied the relationship among sarcopenia and metabolic risk factors in adults over 65 years of age, published between 2012 and 2022 in four databases: PubMed, Web of Science, Cochrane Plus, and CINAHL. A total of 370 articles were identified in the initial search, of which 13 articles were selected for inclusion in this review. It was observed that metabolic risk factors such as Body Mass Index, systolic and diastolic blood pressure, glucose, cholesterol, or triglycerides had a significant association with sarcopenia. There is evidence of the association of different metabolic risk factors with sarcopenia in adults over 65 years of age, so it is necessary to carry out studies that investigate different strategies that reduce the appearance of sarcopenia, and with it, the incidence of metabolic diseases.

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“…As stated earlier, sarcopenia is considered ‘primary sarcopenia’ when it is age‐related and when no other specific causes are evident. It is called ‘secondary sarcopenia’ when sarcopenia occurs resulting from one or more causal factors 7 . Sarcopenia categories are presented in Table 2.…”

Section: Introductionmentioning

confidence: 99%

“…It is called ‘secondary sarcopenia’ when sarcopenia occurs resulting from one or more causal factors. 7 Sarcopenia categories are presented in Table 2 .…”

Section: Introductionmentioning

confidence: 99%

Effects of hormonal changes on sarcopenia in chronic kidney disease: where are we now and what can we do?

Güngör

Ulu

Hasbal

et al. 2021

J cachexia sarcopenia muscle

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Sarcopenia or muscle wasting is a progressive and generalized skeletal muscle disorder involving the accelerated loss of muscle mass and function, often associated with muscle weakness (dynapenia) and frailty. Whereas primary sarcopenia is related to ageing, secondary sarcopenia happens independent of age in the context of chronic disease states such as chronic kidney disease (CKD). Sarcopenia has become a major focus of research and public policy debate due to its impact on patient's health‐related quality of life, health‐care expenditure, morbidity, and mortality. The development of sarcopenia in patients with CKD is multifactorial and it may occur independently of weight loss or cachexia including under obese sarcopenia. Hormonal imbalances can facilitate the development of sarcopenia in the general population and is a common finding in CKD. Hormones that may influence the development of sarcopenia are testosterone, growth hormone, insulin, thyroid hormones, and vitamin D. Although the relationship between free testosterone level that is low in uraemic patients and sarcopenia in CKD is not well‐defined, functional improvement may be seen. Unlike testosterone, it is known that vitamin D is associated with muscle strength, muscle size, and physical performance in patients with CKD. Outcomes after vitamin D replacement therapy are still controversial. The half‐life of growth hormone (GH) is prolonged in patients with CKD. Besides, IGF‐1 levels are normal in patients with Stage 4 CKD—a minimal reduction is seen in the end‐stage renal disease. Unresponsiveness or resistance of IGF‐1 and changes in the GH/IGF‐1 axis are the main causes of sarcopenia in CKD. Low serum T3 level is frequent in CKD, but the net effect on sarcopenia is not well‐studied. CKD patients develop insulin resistance (IR) from the earliest period even before GFR decline begins. IR reduces glucose utilization as an energy source by hepatic gluconeogenesis, decreasing muscle glucose uptake, impairing intracellular glucose metabolism. This cascade results in muscle protein breakdown. IR and sarcopenia might also be a new pathway for targeting. Ghrelin, oestrogen, cortisol, and dehydroepiandrosterone may be other players in the setting of sarcopenia. In this review, we mainly examine the effects of hormonal changes on the occurrence of sarcopenia in patients with CKD via the available data.

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Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital

Cited by 10 publications

References 30 publications

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

Does an Association among Sarcopenia and Metabolic Risk Factors Exist in People Older Than 65 Years? A Systematic Review and Meta-Analysis of Observational Studies

Effects of hormonal changes on sarcopenia in chronic kidney disease: where are we now and what can we do?

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