A rule-based expert system is the simplest form of artificial intelligence and uses prescribed knowledge-based rules to solve a problem 1. The aim of the expert system is to take knowledge from a human expert and convert this into a number of hardcoded rules to apply to the input data. In their most basic form, the rules are commonly conditional statements (if a, then do x, else if b, then do y). These systems should be applied to smaller problems, as the more complex a system is, the more rules that are required to describe it, and thus increased difficulty to model for all possible outcomes.
Note: with problems related to radiological images, often preprocessing of the images is required prior to the expert system being applied 2.
A very basic example of rule-based expert system would be a programme to direct the management of abdominal aneurysms. The system would input the diameter of an aneurysm. Using conditional arguments, the input diameter would be stratified to recommend whether immediate intervention was required, and if not what appropriate follow up is recommended.
- 1. Grosan C., Abraham A. (2011) Rule-Based Expert Systems. In: Intelligent Systems. Intelligent Systems Reference Library, vol 17. Springer, Berlin, Heidelberg
- 2. Stansfield SA. ANGY: A Rule-Based Expert System for Automatic Segmentation of Coronary Vessels From Digital Subtracted Angiograms. IEEE transactions on pattern analysis and machine intelligence. 8 (2): 188-99. Pubmed
Related Radiopaedia articles
- artificial intelligence (AI)
- imaging data sets
- computer-aided diagnosis (CAD)
- natural language processing
machine learning (overview)
- machine learning processes
- machine learning models
- visualizing and understanding neural networks
- common data preparation/preprocessing steps
- DICOM to bitmap conversion
- dimensionality reduction
- principal component analysis
- training, testing and validation datasets
- loss function
- optimization algorithms
- linear and quadratic
- batch normalization
- rule-based expert systems