Background: Botulinum toxin injection is one of the most common esthetic procedures, yet complications may occur due to anatomical variability or suboptimal injection technique. This study aimed to evaluate the upper facial muscles using ultrasound, focusing on inter- and intraindividual variability. Methods: The study involved volunteers aged 21–40 years, excluding those with prior facial treatments, trauma, or muscle disorders. The muscles examined included the occipitofrontalis (frontal belly), procerus, corrugator supercilii, and orbicularis oculi. Muscle thickness and distance from the epidermis were measured using high-frequency ultrasound. Statistical analyses included descriptive statistics, correlation with age and BMI, sex comparisons, and symmetry assessment. Results: A total of 127 participants (103 women and 24 men) were enrolled, with a mean age of 28.8 ± 4.4 years. Age showed no significant correlation with muscle thickness or depth, supporting the internal consistency of the studied age group. BMI showed moderate correlations with the depth of the selected forehead muscles. Males showed greater thickness in the frontal and procerus muscles. Relative side-to-side asymmetry coefficients reached 40% for both thickness and depth, indicating notable individual laterality. Conclusions: The study provides normative ultrasound parameters for the upper facial muscle in healthy adults. The results demonstrate significant anatomical variability depending on sex, BMI, and facial laterality, supporting individualized ultrasound-guided approaches for botulinum toxin injection.

Standard classification methods based on hand-crafted morphological and texture features have achieved good performance in breast mass differentiation in ultrasound (US). In comparison to deep neural networks, commonly perceived as ‘black-box’ models, classical techniques are based on features that have well-understood medical and physical interpretation. However, classifiers based on morphological features commonly underperform in the presence of the shadowing artifact and ill-defined mass borders, while texture based classifiers may fail when the US image is too noisy. Therefore, in practice it would be beneficial to select the classification method based on the appearance of the particular US image. In this work, we develop a deep meta-network that can automatically process input breast mass US images and recommend whether to apply the shape or texture based classifier for the breast mass differentiation. Our preliminary results demonstrate that meta-learning techniques can be used to improve the performance of the standard classifiers based on handcrafted features. With the proposed meta-learning based approach, we achieved the area under the receiver operating characteristic curve of 0.95 and accuracy of 0.91.

An analysis of the methodology used by the authors of the commented article is presented and errors related to data preparation are pointed out.

Understanding the relationship between acoustic properties of breast lesions and resulting ultrasound images may contribute to an earlier and more accurate diagnosis of the most common cancer in women. In addition to in vitro studies, in silico tumor models can provide a lot of crucial information due to the possibility of precise determination of the influence of changes in tissue structure on the resulting ultrasound echoes. The purpose was to develop the numerical phantom of the breast with the tumor for a reliable simulation of ultrasound images. In modeling the tissue structures of the breast, the VICTRE phantom, developed by the FDA for the simulation of X-ray mammography, was used. The numerical ultrasound model of breast cancer allows the simulation of ultrasound signals and images. It could be used to interpret, validate and develop new ultrasound methods for cancer diagnosis.