Furthermore, this study claimed that the SonicBoneTM device takes most of the drawbacks of both the GP atlas and previously suggested QUS-based devices into consideration [22]. A successor to the SonicBoneTM device is the BAUSTTM system, which also performs assessments of bone age using US. Its results are highly reproducible and comparable to bone age assessments based on radiographs [23]. The differences between the determined bone ages of BAUSTTM and assessments of GP and TW3 atlases were found to be non-significant. However, two different parameters from three different locations had to be taken into account to achieve this result. All these systems determine absolute bone age, are mainly used to analyze and predict growth in children, and are unsuitable for identifying an ossified growth plate. In contrast to QUS devices, systems relying on B-mode US images have also been proposed. A study published in 2016 evaluated the postnatal skeletal development of the proximal femoral epiphysis, distal femoral epiphysis, and proximal tibial epiphysis in 178 premature and mature newborns, between 25 and 47 weeks of biological age, and found that B-mode musculoskeletal ultrasound \"is very useful in monitoring the skeletal development of premature and mature newborns\" [24]. Another study published in 2018 examined the wrists of 120 children from 10 to 17 years and found that \"hand-wrist radiographs, which are traditional bone age determination methods, can be replaced with [ultrasonography] in the determination of bone age and especially in determining the extent of pubertal growth in the field of dentistry\" [25]. More recently, a study published in 2021 found that approaches based on B-mode ultrasound \"are of good accuracy in comparison to hand X-ray [but these approaches are] rarely used in daily practice. This may be because the examination needs to be performed by a trained specialist or there is a need for a specific device\" [26]. Another study published in the same year examined the use of ultrasonography for age estimation by investigating the degree of ossification of the distal radial epiphysis on a Turkish population. To this end, \"the left wrist of 688 (322 males, 366 females) patients between the ages of 9 and 25 years was prospectively evaluated by ultrasonography\", and it was found that \"the data obtained may help [to] determine legally critical age limits of 14 and 15\" [27]. In this work, we propose a low-cost, mobile, and custom-designed device, which determines the bone age solely by analyzing 1D US radiofrequency (RF) signals (i.e., A-scans) from the distal epiphyseal plates of the radius bone. We did not design the system to assess legal ages, as there is still a lack of a distinct, universally applicable gold standard due to biases toward white populations in existing approaches [1; 2; 9]. Our device assesses signals from the ulna and radius bones of 120 female subjects from a clinical study to determine the end of bone growth. We evaluated the system using digital signal processing (DSP) and machine learning (ML) methods. Those were traditional algorithms artificial neural networks (ANNs) and gradient boosting machines (GBMs). The traditional algorithms consist of the 1-nearest neighbors algorithm exploiting the dynamic time warping distance (1-NN DTW) [28], support vector machines [29], and logistic regression [30]. The ANN methods include fully convolutional networks (FCNs) [31; 32], multilayer perceptrons (MLPs) [31; 32], residual networks (ResNets) [31; 32], and radial basis function (RBF) neural networks [33]. The GBM approaches are XGBoost [34], LightGBM [35], and CatBoost [36]. To substitute our hypothesis that the determination of the end of bone growth by analyzing 1D US RF signals is possible, we extend previously published preliminary results [37]. We include a variety of different dimensionality reduction techniques (DRTs) and ML models to visualize the acquired data and classify the signals.