Boegl K, Kainberger F, Adlassnig K P, Kolousek G, Leitich H, Kolarz G, Imhof H
Institut für Medizinische Computerwissenschaften, Medizinische Fakultät der Universität Wien.
Radiologe. 1995 Sep;35(9):604-10.
Since the 1960s, several knowledge-based systems for computer-assisted diagnosis in radiology have been developed. The great majority of these tools has been implemented as off-line systems. This requires interaction with the system solely for the purpose of consultation and therefore interrupts the radiologist's work flow. This and inadequate man-machine interfaces may have inhibited the routine clinical use of such systems. The goal of this paper is to describe the current research toward the development of the on-line expert system Cadiag-4/Rheuma-Radio. The underlying fundamentals of the system design, including client/server architecture, communication interfaces, and fuzzy set theory and fuzzy logic as methods for knowledge representation and interference, are presented.
In radiology today, computers are routinely used to acquire radiological images in hospital and radiology information systems (HIS/RIS) and picture archiving and communication systems (PACS). In our approach, we make use of pre-existent sources of information to build an expert system that minimizes the interaction between radiologists and the computer. To handle uncertainty and vagueness of medical knowledge, fuzzy set theory and fuzzy logic are used. Given data of a specific case, a deductive inference procedure combines the observed radiological signs, establishes confirmed and excluded diagnoses as well as diagnostic hypotheses, and provides explanations for these conclusions. Furthermore, proposals for confirmation or exclusion of diagnostic hypotheses are offered.
For evaluation purposes, an early prototype of Cadiag-4/Rheuma-Radio was tested on radiological disorders of the hip joint related to rheumatological diseases. Twenty radiological cases were used as test cases, reaching a diagnostic accuracy of about 80%.
The first results are acceptable and encourage further work to cover the whole area of rheumatologically relevant radiological signs and diagnoses. Furthermore, research into the development of user-oriented data acquisition tools will be carried out.
自20世纪60年代以来，已经开发了几种用于放射学计算机辅助诊断的基于知识的系统。这些工具绝大多数已作为离线系统实施。这仅需要为咨询目的与系统进行交互，因此会打断放射科医生的工作流程。这一点以及人机界面不完善可能抑制了此类系统在临床中的常规使用。本文的目的是描述当前在开发在线专家系统Cadiag - 4/风湿 - 放射学方面的研究。介绍了系统设计的基础原理，包括客户端/服务器架构、通信接口以及作为知识表示和推理方法的模糊集理论和模糊逻辑。
在当今的放射学中，计算机通常用于医院的放射图像采集以及医院信息系统（HIS/RIS）和图像存档与通信系统（PACS）。在我们的方法中，我们利用现有的信息源来构建一个专家系统，以尽量减少放射科医生与计算机之间的交互。为处理医学知识的不确定性和模糊性，使用了模糊集理论和模糊逻辑。给定特定病例的数据，演绎推理程序结合观察到的放射学征象，确立已确诊和排除的诊断以及诊断假设，并为这些结论提供解释。此外，还提供了用于确认或排除诊断假设的建议。
为进行评估，Cadiag - 4/风湿 - 放射学的早期原型在与风湿性疾病相关的髋关节放射学病症上进行了测试。使用了20个放射学病例作为测试案例，诊断准确率约为80%。
初步结果是可接受的，并鼓励进一步开展工作以涵盖与风湿学相关的放射学征象和诊断的整个领域。此外，还将开展面向用户的数据采集工具开发研究。
