RESUMO
BACKGROUND: Various surgical techniques have been devised for the surgical cosmetic enhancement of female outer genitalia. The selection of an optimal method should be based on satisfaction rates and safety; however, a comprehensive, contemporary systematic assessment of these factors in has been limited in the literature. OBJECTIVES: Our aim was to conduct a comprehensive systematic review and meta-analysis to evaluate the overall satisfaction rates and risk factors associated with various labiaplasty techniques and tools. METHODS: The authors performed a systematic literature search in three medical databases: PubMed, Elsevier and Cochrane Library (CENTRAL) with the closing date of October 2023. Original articles with quantitative satisfaction rates and frequencies of most common complications (hematoma, dehiscence, swelling, bleeding and infection) were included. RESULTS: Systematic search provided a total of 3954 records. After selection and review of the articles, 86 eligible, peer-reviewed studies were identified, of which 53 provided quantitative data. High overall satisfaction rate was found for all methods (Prop: 94%; CI: 93-95%), with highest satisfaction for deepithelization (Prop: 97%; CI: 85%-99%). Complications were generally rare, with elevated incidences for some techniques (wedge resection - dehiscence: Prop: 8%; CI: 5%-13% and composite reduction - swelling: Prop: 13%; CI: 2%-54%). Scalpel has significantly higher incidence of complications than laser, namely for bleeding, swelling and hematoma. CONCLUSIONS: Labiaplasty can be considered a generally effective approach to outer female genitalia beautification, with low associated risks. Surgeons must tailor their approach to the patients' needs and anatomy to achieve maximal satisfaction, given the differences in the frequency of complications for each method.
RESUMO
The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our motivation is to take advantage of ML and edge computing and offer innovative solutions to the chemical industry for difficult-to-measure laboratory variables. The purpose of software sensor models is to continuously forecast the quality of products to achieve effective quality control, maintain the stable production condition of plants, and support efficient, environmentally friendly, and harmless laboratory work. As a result of the literature review, quite a few ML models have been developed in recent years that support the quality assurance of different types of materials. However, the problems of continuous operation, maintenance and version control of these models have not yet been solved. The method uses ML algorithms and takes advantage of cloud services in an enterprise environment. Industrial 4.0 devices such as the Internet of Things (IoT), edge computing, cloud computing, ML, and artificial intelligence (AI) are core techniques. The article outlines an information system structure and the related methodology based on data from a quality-assurance laboratory. During the development, we encountered several challenges resulting from the continuous development of ML models and the tuning of their parameters. The article discusses the development, version control, validation, lifecycle, and maintenance of ML models and a case study. The developed framework can continuously monitor the performance of the models and increase the amount of data that make up the models. As a result, the most accurate, data-driven and up-to-date models are always available to quality-assurance engineers with this solution.
