摘要
人工智能(artificial intelligence,AI)系统与超声医疗大数据分析处理技术的相互集成及其运用现已成为医工融合的新热点。AI技术一个主要的核心算法是深度机器学习,它可以智能地识别和分类超声图像。近年来,国内外越来越多从事超声医学工程与计算机科学领域研究的技术专家开始致力于如何推动智能超声+大数据技术的高度融合发展,旨在辅助临床医师进行疾病筛查、诊断、预后风险评估等。这有利于减轻一线医务人员的工作量、提高超声检查在辅助疾病诊断方面的准确度、减少对患者病情的误诊率,从而提升诊治效率,满足了日益增长的各项临床服务需求。本文重点就AI和大数据技术在医院超声检查的临床及实践工作中的应用进行系统探讨,针对AI和大数据在“医工融合”过程中技术的必要性、发展现状及存在的问题逐一进行总结,旨在推动医工融合的可持续发展以及超声医学临床实践水平的提高。
The mutual integration and application of artiflcial intelligence(AI) system and ultrasonic medical big data analysis and processing technology has developed into a new hot spot in the integration of medical industry. One of the most important algorithms at the heart of AI technology is deep machine learning, which can intelligently recognize and classify ultrasonic images.In recent years, more and more technical experts engaged in the fleld of ultrasound medical engineering and computer science at home and abroad have begun to devote themselves to how to promote the highly integrated development of intelligent ultrasound and big data technology, aiming at assisting clinical physicians screening, diagnosis, prognosis risk assessment, etc. It is helpful to reduce the workload of front-line medical staff, increase the accuracy of ultrasonography in assisting disease diagnosis and reduce the misdiagnosis rate of patients’ conditions, so as to improve the diagnosis and treatment efficiency and meet the rapid growth of various clinical service demands. This paper focused on the application of AI and big data technology in the clinical and practical work of hospital ultrasound, and summarized the technical necessity, development status and existing problems of AI and big data in the development of “medical and industrial integration” one by one. It aimed to promote the sustainable development of the integration of medicine and industry and improve the level of clinical practice of ultrasound medicine.
作者
程妙仙
曾令红
吴忧
刘雯
白榕林
李思丹
CHENG Miaoxian;ZENG Linghong;WU You;LIU Wen;BAI Rongling;LI Sidan(School of Medical Imaging,Changsha Medical College,Changsha 410000,Hunan Province,China;Department of Ultrasound in Medicine,The First Affiliated Hospital of Changsha Medical College,Changsha 410000,Hunan Province,China)
出处
《肿瘤影像学》
2023年第1期78-82,共5页
Oncoradiology
基金
国家级大学生创新创业训练计划项目(教高司函[2021]13号-202110823018X)
湖南省普通高校创新创业教育中心基金项目(湘教通[2021]356号-82)。
关键词
医工融合
人工智能
大数据
超声临床实践
深度学习
Medical and industrial integration
Artiflcial intelligence
Big data
Clinical practice of ultrasound
Deep learning
