Sarcopenia: modern approaches to solving diagnosis problems

Abstract

Although sarcopenia is a relatively new diagnosis for medical statistics and the healthcare system, it represents a social and economic burden on the healthcare due to the large number of possible adverse outcomes such as increased risk of falls, physical disability, longer hospital stays, and increased mortality. No specialized medical treatment is available for sarcopenia; however, prevention and timely nonpharmacological treatment can reduce the risk of potential adverse effects. To establish the diagnosis of sarcopenia, it is necessary to confirm the decrease in not only muscle strength but also muscle mass. Instrumental diagnostics includes methods such as dual-energy X-ray absorptiometry and bioimpedance analysis. These methods can be supplemented by artificial intelligence algorithms for the automatic segmentation of muscle and fat tissue on computed tomography and magnetic resonance images, followed by calculation of the skeletal muscle index at the level of the L3 vertebra (L3SMI). Such software, when used in systems such as the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow, opens up opportunities for opportunistic screening. However, despite the recognition of CT and MRI as the gold standard by the European Working Group on Sarcopenia in Older People, there are no generally accepted L3SMI cut-off values for CT and MR diagnostics of sarcopenia. Furthermore, there is the problem of unifying the term skeletal muscle index. If these problems could be solved through further population studies, it will be possible to obtain a new method for the instrumental diagnosis of sarcopenia with its subsequent use for opportunistic screening.

Keywords

• sarcopenia
• frailty
• artificial intelligence
• mass screening