# DeepSarc-US: A Deep Learning Framework for Assessing Sarcopenia Using Ultrasound Images
###### Abstract  
Sarcopenia, the age-related loss of skeletal muscle mass, is a core component of frailty that is associated with functional decline and adverse health events in older adults. Unfortunately, the available tools to diagnose sarcopenia are often inaccessible or not user-friendly for clinicians. Point-of-care ultrasound (US) is a promising tool that has been used to image the quadriceps muscle and measure its thickness (QMT) as a diagnostic criterion for sarcopenia. This measurement can be challenging for clinicians, especially when performed at the bedside using handheld systems or phased-array probes not designed for this use case. In this paper, we sought to automate this measurement using deep learning methods to improve its accuracy, reliability, and speed in the hands of untrained clinicians. In the proposed framework, which aids in better training, particularly when limited data are available, convolutional and transformer-based deep learning models with generic or data-driven pre-trained weights were compared. We evaluated regression (QMT as a continuous output in cm) and classification (QMT as an ordinal output in 0.5 cm bins) approaches, and in the latter, activation maps were generated to interpret the anatomical landmarks driving the model predictions. Finally, we evaluated a segmentation approach to derive QMT. The results showed that both transformer-based models and convolutional neural networks benefit from the proposed framework in estimating QMT. Additionally, the activation maps highlighted the interface between the femur bone and the quadriceps muscle as a key anatomical landmark for accurate predictions. The proposed framework is a pivotal step to enable the application of US-based measurement of QMT in large-scale clinical studies seeking to validate its diagnostic performance for sarcopenia, alone or with ancillary criteria assessing muscle quality or strength. We believe that implementing the proposed framework will empower clinicians to conveniently diagnose sarcopenia in clinical settings and accordingly personalize the care of older patients, leading to improved patient outcomes and a more efficient allocation of healthcare resources. ultrasound; quadriceps muscle thickness; transformer; regression; activation map +
Footnote †: journal: Medical  
surgeons have embraced the concept of frailty to better characterize their older patients, predict risk, and guide treatment decisions. This is crucial to ensuring benefits for patients and avoiding costly yet futile procedures. In addition, it is helpful to prepare patients before and after cardiac surgery through cardiac rehabilitation, exercise programs, nutritional supplementation, and comprehensive geriatric interventions. Consequently, proactive detection of frailty in cardiac patients may allow for the deployment of cost-effective, easy-to-implement preventive health measures that have been shown to improve clinical and patient-reported outcomes [4, 5]. There are multiple ways to operationalize frailty and measure it in the clinical setting [6, 7, 8]. One of the biggest challenges is the lack of clinician-friendly tools to measure frailty in acutely ill patients who are otherwise unable to perform the usual physical performance tests and questionnaires, especially those seen in the emergency department and cardiac intensive care unit. A proposed solution has been to measure muscle mass and quality as an objective biomarker of frailty, which does not require any patient effort to acquire and can be used in all patients, regardless of acuity [9]. The age-related loss of muscle mass and quality is known as sarcopenia, and it is one of the cornerstones of frailty syndrome [10, 11]. Sarcopenia has been defined as a systemic condition that reflects the functional and physical aspects of aging; thus, its assessment in a clinical environment can provide useful information about the patient's underlying frailty [12].