SISTEMA HÍBRIDO DE RECOMENDAÇÃO BASEADO EM DEEP LEARNING PARA PERSONALIZAÇÃO DE TRATAMENTOS ESTÉTICOS FACIAIS: UMA ABORDAGEM QUANTITATIVA

Contemporary aesthetic dermatology faces the challenge posed by the inherent subjectivity of traditional facial aging assessment methods, which limit the standardization and reproducibility of therapeutic outcomes. This article proposes a conceptual architecture for a hybrid recommendation system, based on deep learning technologies, aiming to introduce a quantitative and personalized approach for recommending facial aesthetic treatments. The system employs a computer vision pipeline, consisting of a U-Net neural network for the semantic segmentation of dermal features—such as wrinkles, pores, and pigmentary lesions—and a ResNet network for the extraction of a quantitative feature vector. Subsequently, a hybrid recommendation engine, combining content-based filtering and collaborative filtering, processes this vector to generate an individualized treatment plan. Content-based filtering correlates the patient's dermal features with a knowledge base of procedures, while collaborative filtering refines the suggestions based on successful outcomes in patients with similar dermal profiles. The potential benefits in diagnostic accuracy and therapeutic efficacy are discussed, as well as the ethical and technical challenges, such as algorithmic bias and data privacy. It is concluded that the proposed framework represents a significant step towards evidence-based aesthetic medicine, capable of delivering hyper-personalization at scale and serving as a clinical decision support tool.