人工智能在小儿泌尿外科研究中的应用：数据分析与发现

AI in Pediatric Urology Research: Data Analysis and Discovery

摘要: 小儿泌尿系统疾病作为常见小儿病症，种类繁多且临床表现复杂，对诊断和治疗决策带来了重大挑战。传统研究方法受限于数据获取困难、样本量小及数据标准化不足等问题。人工智能(AI)技术的引入为解决这些问题提供了新途径。在诊断方面，AI技术通过超声和CT影像分析，显著提高了肾积水分级、膀胱输尿管反流评估等医学图像分析的准确性和客观性。在手术治疗中，AI技术通过整合患者的临床特征、遗传信息和影像数据，为医生提供个性化治疗建议。在预后评估方面，基于机器学习和深度学习的预测模型能够融合多维临床数据，对肾盂输尿管连接处梗阻肾盂成形术后复发、后尿道瓣膜预后等提供精准预测，辅助制定个性化治疗及随访策略。尽管AI技术在小儿泌尿外科展现出巨大潜力，但在数据质量、算法透明度及伦理合规等方面仍面临挑战。未来，随着多学科协作的深化和技术的不断进步，建立完善的数据共享机制、开发可解释AI技术、推动AI与临床实践深度融合将成为重要发展方向。本文旨在系统梳理AI技术在小儿泌尿外科研究中的应用现状，分析其在数据分析和知识发现方面的潜力、局限性及未来方向，为推动AI技术在小儿泌尿外科领域的深入应用提供参考。

Abstract: Pediatric urological diseases, as common pediatric conditions, encompass a wide variety of disorders with complex clinical presentations, posing significant challenges for diagnosis and treatment decisions. Traditional research methods are limited by difficulties in data acquisition, small sample sizes, and insufficient data standardization. The introduction of artificial intelligence (AI) technology offers new approaches to address these challenges. In diagnostics, AI technology has significantly improved the accuracy and objectivity of medical image analysis for conditions such as hydronephrosis grading and vesicoureteral reflux evaluation through ultrasound and CT imaging. In surgical treatment, AI technology integrates patients’ clinical characteristics, genetic information, and imaging data to provide personalized treatment recommendations. In prognostic evaluation, prediction models based on machine learning and deep learning can integrate multidimensional clinical data to provide accurate predictions for conditions such as recurrence after pyeloplasty for ureteropelvic junction obstruction and prognosis of posterior urethral valves, assisting in the development of personalized treatment and follow-up strategies. Despite the tremendous potential demonstrated by AI technology in pediatric urology, challenges remain in data quality, algorithm transparency, and ethical compliance. In the future, with deepening multidisciplinary collaboration and continuous technological advancement, establishing comprehensive data sharing mechanisms, developing explainable AI technology, and promoting the deep integration of AI with clinical practice will become key developmental directions. This review aims to systematically examine the current applications of AI technology in pediatric urology research and analyze its potential, limitations, and future directions in data analysis and knowledge discovery, providing a reference for promoting the in-depth application of AI technology in the field of pediatric urology.