1. Computerized Decision Support for Medical Imaging
Authors: C. Shyu, C.Brodley, A. Kak, A. Kosaka, A. Aisen, L. Broderick
Journal: Computer Vision and Image Understanding , Vol. 75, 1999
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2. Computerized decision support systems
Active knowledge systems which use two or more items of patient data to generate case-specific advice.
knowledge systems:
Knowledge source from which the advice is picked: images, algorithms for manipulating the images, medical data, derived probabilities and symbolic representation of medical facts
A mechanism by which a user may quickly derive relevant information from the knowledge source
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3. Computerized decision support systems
General criteria four successful decision support systems:
Need for a decision aid:
Ex: when the interpretation requires specialist expertise, when many images are generated or interpretation is specially difficult because of noise or because the image is visually complicated
Practicality:
the constraints of the medical domain and the clinical setting should be taken into account
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4. Computerized decision support systems
General criteria four successful decision support systems (cont.):
Veracity:
The knowledge source on which the decision support is based must be accurate and complete
Relevance:
The system provides the user with information which improves his/her decision making.
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5. Computer aids for diagnostic radiology
Image databases
Decision support systems based on numerical methods
Use of Bayes’ rule to combine, for example, information about the frequency of a disease and the frequency with which signs are associated with that disease
Expert Systems
they are based on a set of rules which represent the knowledge used by clinicians in making decisions. The rules are used together with information provided by the user to generate inferences.
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6. Architecture of a medical imaging expert system
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7. Applications of the Imaging Expert Systems
Automatic generation of anatomical and functional atlases
CBIR
The Imaging expert systems should improve the diagnostic accuracy by providing a second opinion: they should provide objective measures or normal and abnormal patterns and dray the attention of radiologists
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8. Computer Vision in Medicine
Automatic detection of clustered micro-calcifications on mammograms
Computerized detection of pulmonary nodules
Computerized analysis of heart size
Characterization of interstitial disease on chest radiographs
Automatic tracking of vessels on angiographic images
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9. Computer Vision in Medicine
Low-level features were extracted for the different applications, but they were limited to a certain disease, or organ or pathology
Therefore, the low-level feature extractors should be combined and included in future imaging expert systems.
Multiplying the inputs increases the quantity of information the expert system can use to support diagnosis hypothesis.
Spatial relationships between features
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10. Future trends in Medical Imaging
A new framework for a natural and systematic way of gathering knowledge from domain experts.
Integration of the IES into the PACS systems.
Some preliminary results for digital mammography.
New standards to measure the accuracy of the IES
Subjective assessment of radiologists is often the only reference available
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11. Future trends in Medical Imaging
Future applications: from pure diagnostic purpose to therapeutic management:
Taking into account the reaction of the patient to a certain therapy will provide information for supporting or refining the diagnostic hypothesis.
T-HELPER
Medical training
Quick medical reference ---electronic textbook of medicine
INTERNIST-1
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12. Computerized decision support in diagnostic radiology
What is an expert system?
“A computer system that is programmed to imitate the problem-solving procedures that a human expert makes. For example, in a medical system the user might enter data like the patient's symptoms, lab reports, etc., and derive from the computer a possible diagnosis. The success of an expert system depends on the quality of the data provided to the computer, and the rules the computer has been programmed with for making deductions from that data.” (High-Tech Dictionary)
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