# DeepSarc-US: A Deep Learning Framework for Assessing Sarcopenia Using Ultrasound Images
## 4 Discussion and Conclusions
Furthermore, the application of _Seg-Regression_ is primarily tailored for the precise measurement of muscle thickness and may not be seamlessly extended to assess other essential biomarkers such as hand grip strength, FI, CFS, etc., required for sarcopenia assessments. This constraint underscores the need for a nuanced understanding of the method's scope, emphasizing its suitability for specific QMT-related assessments while recognizing its limitations in a broader context. A novel aspect of this study is the demonstrated ability to generate accurate predictions of QMT despite the use of suboptimal phased-array US source images. Such measurements would otherwise require training, practice, and added time for clinicians to perform manually--all of which are major barriers to clinical translation. Phased-array images were used because of their convenience for clinical acquisition without the need to switch probes in the context of a cardiac US exam. Further research is underway using linear probes in the context of a point-of-care US exam using a handheld system. The images provided by these probes are ideally suited to imaging superficial skeletal muscles and should, in theory, generate even more accurate predictions of QMT. It is important to note that predicting sarcopenia involves more than just measuring muscle thickness. Accurate assessment requires integrating various patient measurements to provide a comprehensive evaluation. The proposed technique is designed to automate the entire sarcopenia assessment process by relying solely on patient data, minimizing the need for practitioner input. This approach is particularly useful in scenarios where immediate access to equipment for measuring muscle thickness is not available, such as bedside data collection. By incorporating our method, we can facilitate the development of fully automated, online sarcopenia assessment systems, enhancing the efficiency and accuracy of patient evaluations. One limitation of this study is that only one leg was imaged. A more accurate approach would involve imaging both legs and averaging the measurements, although this would significantly increase the scan time. Future research could explore the costs and benefits of imaging both legs. Another constraint of the current study is the phased-array transducer that was used for data acquisition. Limb muscles, as mentioned earlier, are typically evaluated using linear probes that are better adapted for superficial structures. However, in the current study, phased-array images were used because of their convenience for clinical acquisition without the need to switch probes in the context of a cardiac US exam. The contribution of this study is a necessary step preceding the clinical application of QMT at scale. The objective was to provide detailed benchmarking and a comparative analysis of the performance of various models, helping us to understanding the strengths and weaknesses of different architectures in the context of muscle thickness measurements. By providing reliable and precise muscle thickness measurements, this study contributes essential data that, when combined with other diagnostic criteria, will help categorize patients more specifically within the spectrum of sarcopenia. Ultimately, this will enhance the ability of healthcare providers to diagnose and manage sarcopenia more effectively. Having derived DeepSarc-US, the stage is now set to apply this method to a large-scale clinical cohort to validate its diagnostic performance against a gold standard determination of sarcopenia. It is important to note that muscle size in isolation may not be sufficient to diagnose sarcopenia, and ancillary criteria assessing muscle quality and strength may very well be needed. Further research involving radiomic features of muscle quality is currently an active area of ongoing investigations that has the potential to complement the proposed measures of muscle size. **Author Contributions:** Conceptualization, B.B., J.A. and H.R. ; methodology, B.B. ; validation, B.B., J.A. and H.R.