Taylor Andrew, Manatunga Amita, Garcia Ernest V
Department of Radiology, Emory University School of Medicine, Atlanta, GA 30322, USA.
Semin Nucl Med. 2008 Jan;38(1):67-81. doi: 10.1053/j.semnuclmed.2007.09.006.
The volume of diagnostic imaging studies performed in the United States is rapidly increasing resulting from an increase in the number of patients as well as an increase in the volume of studies per patient. Concurrently, the number and complexity of images in each patient data set are also increasing. Nuclear medicine physicians and radiologists are required to master an ever-expanding knowledge base whereas the hours available to master this knowledge base and apply it to specific tasks are steadily shrinking. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. The problem is particularly acute for low-volume studies such as MAG3 diuresis renography where many imagers may have had limited training or experience. To address this problem, renal decision support systems (DSS) are being developed to assist physicians evaluate suspected obstruction in patients referred for diuresis renography. Categories of DSS include neural networks, case-based reasoning, expert systems and statistical systems; RENEX and CART are examples of renal DSS currently in development. RENEX (renal expert) uses a set of rules obtained from human experts to analyze a knowledge base of expanded quantitative parameters obtained from diuresis MAG3 scintigraphy whereas CART (classification and regression tree analysis) is a statistical method that grows and prunes a decision tree based on an analysis of these quantitative parameters in a training data set. RENEX can be queried to provide the reasons for its conclusions. Initial data show that the interpretations provided by RENEX and CART are comparable to the interpretations of a panel of experts blinded to clinical information. This project should serve as a benchmark for the scientific comparison and collaboration of these 2 fields of medical decision-making. Moreover, we anticipate that these DSS will better define the essential interpretative criteria, foster standardized interpretation, teach trainees to better interpret renal scans, enhance diagnostic accuracy and provide a methodology applicable to other diagnostic problems in radiology and medicine.
在美国，由于患者数量增加以及每位患者的检查量增多，诊断成像研究的数量正在迅速增长。与此同时，每个患者数据集中图像的数量和复杂性也在增加。核医学医师和放射科医生需要掌握不断扩展的知识库，而用于掌握该知识库并将其应用于特定任务的时间却在稳步减少。不断扩展的知识库与日益紧迫的时间限制相互交织，增加了医生出错的可能性。对于诸如MAG3利尿肾造影等检查量较少的研究来说，这个问题尤为严重，因为许多成像人员可能接受的培训有限或经验不足。为了解决这个问题，正在开发肾脏决策支持系统（DSS），以协助医生评估因利尿肾造影而转诊的患者是否存在疑似梗阻。DSS的类别包括神经网络、基于案例的推理、专家系统和统计系统；RENEX和CART是目前正在开发的肾脏DSS的例子。RENEX（肾脏专家）使用从人类专家那里获得的一组规则，来分析从利尿MAG3闪烁扫描中获得的扩展定量参数的知识库，而CART（分类与回归树分析）是一种统计方法，它基于对训练数据集中这些定量参数的分析来生长和修剪决策树。可以查询RENEX以了解其得出结论的原因。初步数据表明，RENEX和CART提供的解读与一组对临床信息不知情的专家的解读相当。该项目应成为这两个医学决策领域进行科学比较与合作的基准。此外，我们预计这些DSS将更好地界定基本的解读标准，促进标准化解读，教导学员更好地解读肾脏扫描，提高诊断准确性，并提供一种适用于放射学和医学中其他诊断问题的方法。
