Application progress of artificial intelligence and big data in ultrasound medicine practice

CHENG Miaoxian; ZENG Linghong; WU You; LIU Wen; BAI Rongling; LI Sidan

doi:10.19732/j.cnki.2096-6210.2023.01.012

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Application progress of artificial intelligence and big data in ultrasound medicine practice

更新时间：2025-12-15

- Application progress of artificial intelligence and big data in ultrasound medicine practice
- Vol. 32, Issue 1, Pages: 78-82(2023)
- 作者机构：
  
  1. 长沙医学院医学影像学院,湖南,长沙,410000
  2. 长沙医学院附属第一医院超声科,湖南,长沙,410000
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2023.01.012
  CLC：
- Published Online：28 February 2023，
  
  Published：28 February 2023
- 稿件说明：
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程妙仙,曾令红,吴忧,等. 人工智能与大数据在超声医学实践中的应用进展[J]. 肿瘤影像学, 2023, 32(1): 78-82 https://doi.

org/10.19732/j.cnki.2096-6210.2023.01.012
程妙仙,曾令红,吴忧,等. 人工智能与大数据在超声医学实践中的应用进展[J]. 肿瘤影像学, 2023, 32(1): 78-82 https://doi. DOI： 10.19732/j.cnki.2096-6210.2023.01.012.

org/10.19732/j.cnki.2096-6210.2023.01.012 DOI：

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。

Abstract

The mutual integration and application of artificial 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 field 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.

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