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本文引用的文献

1
Endoscopic third ventriculostomy: predicting outcome with phase-contrast MR imaging.
Radiology. 2009 Sep;252(3):825-32. doi: 10.1148/radiol.2523081398. Epub 2009 Jul 8.
2
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Radiology. 2009 Apr;251(1):147-55. doi: 10.1148/radiol.2511081174. Epub 2009 Feb 12.
3
Potential surrogate markers of cerebral microvascular angiopathy in asymptomatic subjects at risk of stroke.无症状卒中高危人群脑小血管病的潜在替代标志物。
Eur Radiol. 2009 Apr;19(4):1011-8. doi: 10.1007/s00330-008-1202-8. Epub 2008 Nov 6.
4
Magnetic resonance perfusion imaging in neuro-oncology.神经肿瘤学中的磁共振灌注成像
Cancer Imaging. 2008 Oct 13;8(1):186-99. doi: 10.1102/1470-7330.2008.0019.
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Decision support systems in diuresis renography.利尿肾图中的决策支持系统。
Semin Nucl Med. 2008 Jan;38(1):67-81. doi: 10.1053/j.semnuclmed.2007.09.006.
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Changing concepts of cerebrospinal fluid hydrodynamics: role of phase-contrast magnetic resonance imaging and implications for cerebral microvascular disease.脑脊液流体动力学概念的转变:相位对比磁共振成像的作用及其对脑微血管疾病的影响
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Neural network predictions of significant coronary artery stenosis in men.男性显著冠状动脉狭窄的神经网络预测
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临床放射实践的决策支持系统--迈向下一代。

Decision support systems for clinical radiological practice -- towards the next generation.

机构信息

Department of Imaging Science, University of Manchester, Wolfson Molecular Imaging Centre, Manchester, UK.

出版信息

Br J Radiol. 2010 Nov;83(995):904-14. doi: 10.1259/bjr/33620087.

DOI:10.1259/bjr/33620087
PMID:20965900
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3473729/
Abstract

The huge amount of information that needs to be assimilated in order to keep pace with the continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. Within radiology, the recent development of quantitative imaging techniques, such as perfusion imaging, and the development of imaging-based biomarkers in modern therapeutic assessment has highlighted the need for computer systems to provide the radiological community with support for academic as well as clinical/translational applications. This article provides an overview of the underlying design and functionality of radiological decision support systems with examples tracing the development and evolution of such systems over the past 40 years. More importantly, we discuss the specific design, performance and usage characteristics that previous systems have highlighted as being necessary for clinical uptake and routine use. Additionally, we have identified particular failings in our current methodologies for data dissemination within the medical domain that must be overcome if the next generation of decision support systems is to be implemented successfully.

摘要

为了跟上现代医学实践的持续进步,需要吸收大量的信息,这可能会成为个体临床医生难以逾越的障碍。在放射学领域,定量成像技术(如灌注成像)的最新发展,以及基于成像的生物标志物在现代治疗评估中的发展,突出了计算机系统为放射科医生提供学术和临床/转化应用支持的必要性。本文概述了放射学决策支持系统的基本设计和功能,并举例说明了过去 40 年来这些系统的发展和演变。更重要的是,我们讨论了以前的系统所强调的、对于临床应用和常规使用所必需的特定设计、性能和使用特点。此外,如果要成功实施下一代决策支持系统,我们还必须克服当前在医疗领域内数据传播方法中存在的特定缺陷。