# DeepSarc-US: A Deep Learning Framework for Assessing Sarcopenia Using Ultrasound Images
###### Abstract
When evaluating sarcopenia, various physical tests and questionnaires are available, including assessments of hand grip strength, muscle mass, clinical frailty scale (CFS), frailty index (FI), and others [13, 14]. Measurement of muscle mass can be achieved using a variety of imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), dual X-ray absorptiometry, or ultrasound (US) [15, 16, 17]. Additionally, measurement of intramuscular fat and inflammation (indicative of muscle quality) can be achieved using many of these modalities. US, compared to other modalities, has the major advantage of being portable and feasible at the patient's bedside, which is particularly attractive for acutely ill patients that cannot otherwise be effectively transported for such tests. In addition, US is a non-invasive, portable, cost-effective, and safe imaging modality that is widely used in medicine [18]. The evidence for using US to measure the quality and quantity of a variety of muscle groups is compelling [17, 19, 20]. Unfortunately, US has noteworthy drawbacks such as a low inherent signal-to-noise ratio, low contrast to differentiate muscle from adjacent soft tissues, operator-dependent acquisition of images, and the need for significant time and training to quantitatively measure muscle thickness and intramuscular fat. Progress is needed to overcome these drawbacks before US can become a mainstream tool for the assessment of sarcopenia by clinicians. Recent developments in image processing techniques such as deep learning (DL), particularly convolutional neural networks (CNNs) and transformers, have been instrumental in automating and standardizing the analysis of medical images and deriving informative features not otherwise apparent to the human eye. [PERSON] et al. developed a DL method that allowed for the automated and reliable quantification of skeletal muscle mass for sarcopenia assessment from CT images, which may be integrated into a clinical workflow [15]. [PERSON] et al. used DL methods to optimize and improve cardiac US images in hospitalized patients with chronic heart failure (CHF). Their findings further revealed a correlation between CHF and sarcopenia [21]. [PERSON] et al. used an autoencoder DL method to condense valuable information from multi-frame US muscle images, followed by a classification strategy for the diagnosis of sarcopenia [22]. In a similar study, [PERSON] et al. compared several DL methods to diagnose sarcopenia and differentiate it from normal muscle tissue [23]. Yet another study used DL methods to segment muscle tissue from US images [24]. ConvNext is an advanced CNN model showcasing notable performance in various computer vision applications [25]. It leverages a sophisticated architecture, incorporating deep convolutional layers that enable it to capture intricate patterns and features within images. The model benefits from its ability to automatically learn hierarchical representations, making it well-suited for complex image recognition tasks. ConvNext has demonstratedcompetitive accuracy and efficiency, making it a valuable asset in the realm of DL-based image analysis, specifically in US image analysis [26; 27; 28]. Current vision-transformer (ViT) models, inspired by natural language processing (NLP) studies, have shown promising performances compared to CNNs [29]. ViT-based models do not have the inductive locality bias of CNNs, which makes CNNs less effective at modeling long-range dependencies. Instead, ViT-based models take advantage of their data-driven self-attention mechanism, which helps them to better understand the contextual information derived from not only the region of interest but also its surrounding [29; 30; 31]. Despite all the aforementioned benefits compared to CNNs, these models are data-hungry, and their performance can be limited by the size of the training dataset. To tackle this limitation of ViT-based models, [PERSON] et al.