Knowledge-based computer-aided decision support in prenatal toxoplasmosis screening (TempToxopert)

The development of TempToxopert aimed at assisting clinicians in analysing the results of prenatal toxoplasmosis screening tests. Expert knowledge about diagnostics, screening strategies, and treatment of toxoplasmosis during pregnancy was collected and represented as a rule-based decision graph. Based on actual and past individual findings, the system generates case-specific interpretative reports consisting of a diagnostic hypothesis, recommendations for further treatment, and interpretations of specific test results.

Differential diagnosis of rheumatic diseases using conventional radiography

The approach to the differential diagnosis of rheumatic diseases using conventional radiography is systematic and module-oriented, which, with respect to future developments, forms the basis for computer-assisted diagnosis (CAD). The indications follow consensus-based referral criteria and attempts should be made to raise the evidence level of the recommendations. Investigation techniques have been improved in the last few years with the use of digital radiography. New imaging technologies may be available in the future that will achieve at least the same quality of images, while exposing patients to a significantly lower radiation dose. The interpretation of radiographical signs could be enhanced through a correlation with other imaging modalities. Computer-assisted techniques with image processing tools for automated measurements, lesion detection and in the form of expert systems are under development. With conventional radiography embedded in CAD systems, promising options will be available to enhance the differential diagnosis of rheumatic diseases.

Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system

This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.

[Radiologic quantification of joint changes. A methodological overview]

Osteoarthritis as well as rheumatoid arthritis lead to chronical progressive destruction of diseased joints. As aggressive new treatments need to be evaluated, plenty of (semi-) quantitative methods for the radiological joint evaluation had been developed. They lack sufficient reproducibility due to their low objectivity. Modern approaches of computer-assisted radiological quantification should increase the reproducibility and efficiency of radiological scoring. Automatically calculated, computer-assisted measurements of joint space, cartilage- and synovial volume, periarticular assessment of bone mineral density and quantitative analysis of the subchondral plate will have major impact on the radiological routine of the future.

The knowledge model of MedFrame/CADIAG-IV

The medical consultation system MedFrame/CADIAG-IV is a successor of the prior CADIAG projects. It is the result of a complete redesign to account for today's demands on state-of-the-art software. Its knowledge representation and inference process are based on fuzzy set theory and fuzzy logic. Fuzzy sets are used for conversions from measured numeric values and observational data into symbolic ones. Medical relationships between findings, diseases, and therapies, the rules, are represented by fuzzy relations, that express positive or negative associations. Findings, diseases, and therapies are organised in hierarchies.

[New approaches to computer-assisted diagnosis of rheumatologic diseases]

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.

METHODS

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.

RESULTS

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%.

CONCLUSION

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.