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

Dilmurod Turimov Mustapoevich¹, Wooseong Kim¹

¹Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea.

Healthcare (Basel, Switzerland)

|September 28, 2023

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Sarcopenia, a loss of muscle, is challenging to define and measure globally. Modern technologies like AI and wearables show promise for its detection and management, but standardization and data challenges remain.

Area of Science:

Gerontology
Biomedical Engineering
Data Science

Background:

Sarcopenia is characterized by loss of muscle mass, stamina, and physical performance.
Global standardization for sarcopenia definition and measurement is lacking.
Existing diagnostic criteria (EWGSOP, AWGSOP) vary, hindering cross-study comparisons.

Purpose of the Study:

To review sarcopenia detection and management using contemporary technologies.
To examine challenges in sarcopenia diagnosis and data handling.
To explore the potential of emerging technologies in addressing these challenges.

Main Methods:

Literature review of sarcopenia research.
Analysis of machine learning applications in sarcopenia detection.

Keywords:

AWGSOP EWGSOP ML algorithms physical performance sarcopenia

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Investigation of wearable devices for monitoring sarcopenia.

Exploration of blockchain and edge computing for healthcare data management.

Main Results:

Machine learning shows potential for sarcopenia detection but faces data challenges.
Wearable devices offer insights into sarcopenia progression and self-management.
Blockchain and edge computing can enhance health data security and privacy, despite limitations.

Conclusions:

Modern technologies hold significant potential for improving sarcopenia detection and management.
Further research is crucial to address standardization, data management, and effective technology integration.
Standardized approaches and robust data handling are essential for advancing sarcopenia care.