# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 1 Introduction  
Muscle diseases are a significant public health issue and are known to increase mortality and morbidity risk factors [1]. Because of this risk, high rates of muscle disease investigation are common in clinical practice. [PERSON] et al. described muscle problems in clinics, where 53.30% assessed muscle mass daily in clinical practice, 54.50% assessed muscle strength problems, and around 60% of geriatrics, endocrinologists, and rheumatologists estimated muscle mass [2]. The massive prevalence of muscle disease investigation in clinical practice can be supported with ultrasound devices, which offer responsivenessand precision for detecting muscles disease in the range of 90 to 95% [3]. Muscle ultrasound images are a beneficial tool for investigating muscle disease in clinical and research environments [4]. Ultrasonography has transformative potential for measuring muscle health with the emerging interest in documenting and understanding muscle atrophy and function in every patient condition [5]. Additionally, ultrasound characteristics pose specific challenges in 1D [6], 2D [7], 3D [8], and 4D [9] images, such as image quality due to noise, scarce data, low image quality, and hand-held or operator skill, which may have a substantial influence on the efficacy of deep learning performance when compared with X-ray, computed tomography (CT), or magnetic resonance imaging (MRI) images [10] due to the special attention and resources required in the deep learning process. Furthermore, the challenges in the muscle ultrasound imaging process include the identification of landmarks, reliability testing, muscle site tracking, image acquisition and analysis, equipment use, normative data, and the interpretation of results [11; 12]. The limitations of muscle ultrasound imaging can be overcome with good training for clinical personnel and clinical experience [4], and such practice is common in clinics and is painless and noninvasive [13]; however, it is costly and time consuming. Deep learning is an upcoming solution for the detection of landmarks [14], muscle site tracking, reliability checking, and the analysis and interpretation of ultrasound images which may support novice clinical personnel [15; 16]. Deep learning has an advantage over other classification and segmentation strategies featuring artificial intelligence in that the efficiency of segmentation can be greatly improved through different sets of filters that can be implemented for each stage in a convolutional neural network (CNN) based and an overall deeper network structure. Methods supervised by deep learning methods are expected to be superior because they use pixel-level annotation, which provides crucial information for dealing with various intensity patterns, especially at the edges of muscle ultrasound images [17; 18; 19]. Deep learning in ultrasound images has been used to support clinical tasks since 2015 [20], such as identification of the abdominal plane [21; 22], frame labeling on fetal pregnancy images [23], analyzing abdominal circumference [24], vessel detection [25], tongue contour extraction [26], carotid plaque classification [27], thyroid nodule detection [28], and the identification liver cancer [29]. Studies on deep learning ultrasound images with organs, the thyroid, breast, liver, heart, bone, and brain can be found in the literature [20]; however, few studies in the literature on deep learning muscle ultrasound images in skeletal and smooth muscles describe or summarize the strategies required to overcome the inherent challenges. In 2018 (Figure 1), deep learning was increasingly used to support skeletal muscle and smooth muscle ultrasound imaging in order to improve reliability testing for the classification of muscle types [30], classifying muscles by gender [31], and the classification of muscle vibration [32]. Additionally, it supported the identification of landmarks such as segmentation in the orientation of muscle fibers [33] and tracking the cross-sectional area of the rectus femoris [34].