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

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.

Figure 1

The decision algorithms in modern decision support system (DSS) are commonly complex and designed with recognition that the available information might be incomplete or poorly defined. For instance, in (A) if a choice algorithm states “if a lymph node is >1 cm in short axis diameter then it is pathological” this decision will be subject to the same errors whether it is applied by a human or is automatically implemented. For this reason, in (B) a modern DSS would assess a lymph node as follows: “if a lymph node is >1 cm in short axis diameter and has a fatty hilum, is ovoid in shape and is unchanged over 2 years in a patient with no other tumour markers, then it is likely to be benign”.

Figure 2

Diagrammatic representation of the five components of a rule-based expert system. (1) The knowledge base contains information provided by the domain expert and is used for problem solving in the form of “rules” that usually have a condition/action (if/then) structure. (2) The database contains the facts that have been provided to match against the condition part of the rules. Using the database, the system can search for the appropriate “if” statement to be satisfied before triggering the action structure. (3) The inference engine carries out this reasoning by linking the knowledge base with the facts in the database. (4) The explanation facilities enable the user to query the system as to how a particular conclusion was reached or why a specific fact was/is needed. These facilities also allow the user to interrogate the system as to the rules and knowledge stored within the database and knowledge base. (5) The user interface is not only important in facilitating accurate and easy information provision, but is invaluable in determining the accuracy and methodology of the data visualisation provided to the end user.

Figure 3

Example of a typical neural network; in this case the network is used for the diagnosis of interstitial lung disease. The network shows multiple input nodes with two hidden nodes. These hidden nodes act as a form of feature analysis and detection system; they exert both positive and negative effects on the output diagnosis nodes dependent on their inputs. (Figure reproduced with permission from Asada et al [25].)

Figure 4

Example of a complex Bayesian network for mammography. All nodes can be thought of as representing specific variables relating to the root node such as diagnostic tests, patient demographics, clinical findings and so on. Each node is connected to the other nodes over which they exert influence by arcs that depict the fact that the nodes can affect the probability of the nodes to which they are connected. Ca⁺⁺, calcifications; FHx, family history of breast cancer; HRT, hormone replacement therapy; LN, intramammary lymph node; mass P/A/O, mass present, absent or partially obscured. (Figure reproduced with permission from Burnside [44].)

Figure 5

Simple Bayesian network relating subarachnoid haemorrhage to associated CT scan of the brain. In this case the parent node (as well as the primary node) is subarachnoid haemorrhage with its arc pointing to CT brain (the child node).