Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning

Author:

Tenajas Rebeca1,Miraut David2ORCID,Illana Carlos I.2,Alonso-Gonzalez Rodrigo3ORCID,Arias-Valcayo Fernando4ORCID,Herraiz Joaquin L.45ORCID

Affiliation:

1. Family Medicine Department, Centro de Salud de Arroyomolinos, Arroyomolinos, 28939 Madrid, Spain

2. Advanced Health Technology Department, GMV, Tres Cantos, 28760 Madrid, Spain

3. Emergency Radiology Department, La Paz University Hospital, 28046 Madrid, Spain

4. Nuclear Physics Group and IPARCOS, Faculty of Physical Sciences, University Complutense of Madrid, CEI Moncloa, 28040 Madrid, Spain

5. Health Research Institute of the Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain

Abstract

Ultrasound (US) is a flexible imaging modality used globally as a first-line medical exam procedure in many different clinical cases. It benefits from the continued evolution of ultrasonic technologies and a well-established US-based digital health system. Nevertheless, its diagnostic performance still presents challenges due to the inherent characteristics of US imaging, such as manual operation and significant operator dependence. Artificial intelligence (AI) has proven to recognize complicated scan patterns and provide quantitative assessments for imaging data. Therefore, AI technology has the potential to help physicians get more accurate and repeatable outcomes in the US. In this article, we review the recent advances in AI-assisted US scanning. We have identified the main areas where AI is being used to facilitate US scanning, such as standard plane recognition and organ identification, the extraction of standard clinical planes from 3D US volumes, and the scanning guidance of US acquisitions performed by humans or robots. In general, the lack of standardization and reference datasets in this field makes it difficult to perform comparative studies among the different proposed methods. More open-access repositories of large US datasets with detailed information about the acquisition are needed to facilitate the development of this very active research field, which is expected to have a very positive impact on US imaging.

Funder

Spanish Ministry of Economic Affairs and Digital Transformation

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference72 articles.

1. Computer-Based Monitoring of Cardiovascular Functions in Postoperative Patients;Warner;Circulation,1968

2. A History of the Shift Toward Full Computerization of Medicine;Ambinder;J. Oncol. Pract.,2005

3. Turing, A.M. (1992). Intelligent Machinery, 1948, 4. Reprinted in Mechanical Intelligence (Collected Works of AM Turing), North-Holland Publishing Co.

4. With an Eye to AI and Autonomous Diagnosis;Keane;NPJ Digit. Med.,2018

5. Basics of Deep Learning: A Radiologist’s Guide to Understanding Published Radiology Articles on Deep Learning;Do;Korean J. Radiol.,2020

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3