护理点超声中的人工智能概述。呼吸系统诊断的新视野。

IF 1.6 Q2 ANESTHESIOLOGY Anaesthesiology intensive therapy Pub Date : 2024-01-01 DOI:10.5114/ait.2024.136784
Sławomir Mika, Wojciech Gola, Monika Gil-Mika, Mateusz Wilk, Hanna Misiołek
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引用次数: 0

摘要

在过去的几十年中,如果将超声波检查作为常规床旁检查的一部分,那么超声波检查并没有被证明是一种首选的检查方法。原因是人们认为超声波无法穿透肺部和骨骼结构。直到最近几年,在临床日常工作中使用这种工具的方法才发生了巨大变化,提供了所谓的床旁超声检查(POCUS)。纵向和横向伪影都成为有关病人临床状况的宝贵信息来源,从而帮助医生对病人进行鉴别诊断和监测。重要的是,这些信息都是实时提供的,而且操作本身也是非侵入性的。下一阶段,以机器学习算法为基础的人工智能(AI)的发展将标志着成像诊断领域的进步。本文旨在介绍超声系统现有的创新解决方案,包括智能 B 线技术,以确保自动识别过程以及对受检患者特定肺部区域的 B 线进行解读。文章总结了超声伪影和人工智能在 POCUS 中的应用现状。
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Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.

Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as part of the routine bedside examination. The reason was the assumption defining the lungs and the bone structures as impenetrable by ultrasound. Only during the recent several years has the approach to the use of such tool in clinical daily routines changed dramatically to offer so-called point-of-care ultrasonography (POCUS). Both vertical and horizontal artefacts became valuable sources of information about the patient's clinical condition, assisting therefore the medical practitioner in differential diagnosis and monitoring of the patient. What is important is that the information is delivered in real time, and the procedure itself is non-invasive. The next stage marking the progress made in this area of diagnostic imaging is the development of arti-ficial intelligence (AI) based on machine learning algorithms. This article is intended to present the available, innovative solutions of the ultrasound systems, including Smart B-line technology, to ensure automatic identification process, as well as interpretation of B-lines in the given lung area of the examined patient. The article sums up the state of the art in ultrasound artefacts and AI applied in POCUS.

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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
48
审稿时长
25 weeks
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