AI-BASED ALGORITHM FOR DIAGNOSING DEEP BITE IN CHILDREN

Deep bite is one of the most common dentofacial anomalies in children and adolescents. Timely and accurate diagnosis of this pathology is crucial for selecting the optimal orthodontic treatment strategy and preventing the development of functional and morphological disorders. The aim of this study was to develop and clinically evaluate an algorithm for diagnosing deep bite in children using artificial intelligence technologies. Clinical, anthropometric, photometric, and radiological examination methods were used in the study, along with computer analysis of lateral cephalograms and digital dental models. The proposed AI algorithm made it possible to automate the analysis of diagnostic data, improve the accuracy of determining the type of deep bite, and optimize orthodontic treatment planning. The use of intelligent diagnostic systems contributes to reducing clinician variability and improving the effectiveness of orthodontic care for children.深覆合（Deep bite）是儿童与青少年群体中最常见的牙颌面畸形之一。对该病症开展及时且精准的诊断，是遴选最优正畸治疗策略、预防功能与形态障碍进展的核心前提。本研究旨在开发并临床评估一款依托人工智能技术的儿童深覆合诊断算法。本研究采用了临床检查、人体测量、光度成像及放射学检查手段，并辅以头颅侧位片（lateral cephalograms）与数字化牙科模型（digital dental models）的计算机分析。 所提出的人工智能算法可实现诊断数据分析的自动化，提升深覆合分型判定的精准度，并优化正畸治疗规划流程。智能诊断系统的应用，有助于缩小临床医师间的诊断差异，提升儿童正畸诊疗的整体有效性。