Introduction
The last decades have brought new application areas for ultrasound imaging, including dermatology, allergology, cosmetology, aesthetic medicine, dermatological oncology, and rheumatology. In response to the appearing works in this area, the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) provides the Position Statement on Dermatologic UltrasoundAccording to the newest reportsThe second widely explored area of HFUS application is the assessment of inflammatory skin diseasesIn aesthetic medicine, the HFUS is applicable to support the fillers injection, measure dermal thickness before the aesthetic procedure, or monitor the treatment resultsIn cosmetology, among others the HFUS is used for skin aging assessment. The sought features in this application area are: the arrangement of collagen and elastin bundles, water loss, and presence/parameters of SLEB, which tend to change with ageSince, despite all the benefits of using ultrasound, it is not commonly used in cosmetological practice, the number of papers describing its application in this area still needs to be increased. Therefore, this work presents the first usage of HFUS in assessing topical anti-aging therapy with trichloroacetic acid (TCA) chemical peel. Chemical peels are applied in anti-aging therapy to smooth the surface and remove the top layer of the callous epidermis. They are claimedThe current work proposes quantitative HFUS parameters for evaluating the applied therapy. The measurements of HFUS images acquired with DUB SkinScanner75 (24 MHz transducer) include epidermal thickness and echogenicity, dermal echogenicity in different dermis regions - upper and lower dermis, LEP, MEP, and HEP values of different skin areas, as well as textural features describing the visual appearance of them. Utilizing the benefits of currently developed image processing techniques, the image selection and epidermis segmentation steps presented in our work are fully automated, making the analysis repeatable and reliable. In both steps, we introduce deep neural network models pre-trained on the publicly available HFUS image data