中国血液净化 ›› 2024, Vol. 23 ›› Issue (02): 125-129.doi: 10.3969/j.issn.1671-4091.2024.02.010

• 综述 • 上一篇    下一篇

基于人工智能及音频技术监测动静脉内瘘的研究进展

王凡立    徐元恺    张丽红    杨艳丽   

  1. 050030 石家庄,1河北医科大学第一医院肾内科
    310030 杭州,2浙江医院肾内科
  • 收稿日期:2023-10-09 修回日期:2023-11-13 出版日期:2024-02-12 发布日期:2024-02-12
  • 通讯作者: 张丽红 E-mail:xgtl20ll@163.com
  • 基金资助:
    河北省卫生健康创新专项(22377794D)

Research progress in the monitoring of arteriovenous fistula based on artificial intelligence and audio technology

WANG Fan-li, XU Yuan-kai, ZHANG Li-hong, YANG Yan-li   

  1. Department of Nephrology, The First Hospital of Hebei Medical University, Shijiazhuang 050030, China; 2Department of Nephrology, Zhejiang Hospital, Hangzhou 310030, China
  • Received:2023-10-09 Revised:2023-11-13 Online:2024-02-12 Published:2024-02-12
  • Contact: 050030 石家庄,1河北医科大学第一医院肾内科 E-mail:xgtl20ll@163.com

摘要: 血液透析是终末期肾病主要的肾脏替代治疗方式,自体动静脉内瘘(arteriovenous fistula,AVF)是各大指南推荐的首选血管通路。但反复的AVF失功不仅影响患者生存质量,亦增加巨大的经济、社会负担。因此对AVF功能及时评估并适时给予干预措施至关重要。而相较于物理检查,人工智能因其可以实现检查结果的精确量化、诊疗同质化及远程诊疗而成为研究热点。本文主要对AVF声学特征、声学特征提取方法以及机器学习方法的选择、AVF人工智能监测系统的开发3个方面的研究进展做综述,以期梳理研究脉络,探索临床研究方向。

关键词: AVF, 人工智能, 机器学习, 音频

Abstract: Hemodialysis is the mainstay of renal replacement therapy for end-stage renal disease, and arteriovenous fistula (AVF) is the preferable method for vascular access recommended by major guidelines. However, repeated AVF failures affect the quality of life of the patients, and increase economic and social burdens. Therefore, continuous assessment of AVF function and early intervention to abnormal AVF is essential. Currently, artificial intelligence has become a hot issue due to the advantages of accurate and quantified results, homogenized and remote diagnosis and treatment, as compared to the physical examination of AVF. In this article, research progresses in AVF acoustic feature and its extraction method, selection of machine learning method, and the development of AVF monitoring system by artificial intelligence are reviewed in order to explore the research pathways and the direction of clinical research.

Key words: Arteriovenous fistula, Artificial intelligence, Machine learning, Audio

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