title: ÐÐ¾Ð²ÑÑÐµÐ½Ð¸Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ Ð² ÑÑÐ¾Ð¼Ð°ÑÐ¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¸ ÑÑÑÐµÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½Ðµ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Improving the quality of diagnostics in dentistry and aesthetic medicine using machine learning methods
Creators: name: ÐÐ¾Ð»ÑÑÐ½Ð¾Ð²ÑÐºÐ¸Ð¹, ÐÐ¸ÐºÐ¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐ¾ÑÐ¾Ð¿ÑÐ¾ÑÐ¾ÐºÐ¾Ð»
Ð°Ð½Ð°Ð»Ð¸Ð·
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð»Ð¸ÑÐµÐ²Ð¾Ð¹ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑ
photo protocol analysis
neural network
facial parameter

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description: This work is devoted to the study and application of approaches to analyzing human aesthetic parameters, methods for optimizing diagnostics based on the work of machine learning models. The research set the following goals: 1) Study of the features of processing the primary photo protocol of patients in dentistry and aesthetic medicine. 2) Identification of relevant machine learning methods for automating the processing of patient graphic data. 3) Development of solutions for automating the processing of patient photo protocol data. 4) Creation of tools using machine learning methods for analyzing patient facial symmetries. Based on the results of the work, modern methods of face detection on primary photo protocols and data processing for their application in medical tasks were analyzed. The experiments confirmed the importance of high-quality data preparation and showed that the use of deep neural networks requires large and balanced data sets. The implementation of key facial point detectors made it possible to visualize facial symmetries, which can significantly facilitate the work of medical specialists. It was found that the 68-point dlib model is best suited for searching for facial parameter lines, and the Xception architecture demonstrates promising results. As a result, a software solution was developed for the step-bystep analysis of photo protocols.