Neural Networks in Medicine

written by Dimitrios Siganos

Medicine has always benefited from the forefront of technology. Technology advances like computers, lasers, ultrasonic imaging, etc. have boosted medicine to extraordinary levels of achievement. Artificial Neural Networks (ANN) is currently the next promising area of interest. It is believed that neural networks will have extensive application to biomedical problems in the next few years. Already, it has been successfully applied to various areas of medicine, such as diagnostic systems, biochemical analysis, image analysis, and drug development.

Diagnostic systems
ANNs are extensively used in diagnostic systems. They are normally used to detect cancer and heart problems. The benefits of using ANNs is that they are not affected by factors such as fatigue, working conditions and emotional state.

Biochemical Analysis
ANNs are used in a wide variety of analytical chemistry applications. In medicine, ANNs have been used to analyse blood and urine samples, track glucose levels in diabetics, determine ion levels in body fluids, and detect pathological conditions such as tuberculosis.

Image analysis
ANNs are used in the analysis of medical images from a variety of imaging modalities. Applications in this area include tumour detection in ultra-sonograms, classification of chest x-rays, tissue and vessel classification in magnetic resonance images (MRI), determination of skeletal age from x-ray images, and determination of brain maturation.

Drug development
ANNs are used as tools in the development of drugs for treating cancer and AIDS. ANNs are also used in the process of modelling biomolecules.

Modeling and Diagnosing the Cardiovascular System
Neural Networks are used experimentally to model the human cardiovascular system. Diagnosis can be achieved by building a model of the cardiovascular system of an individual and comparing it with the real time physiological measurements taken from the patient. If this routine is carried out regularly, potential harmful medical conditions can be detected at an early stage and thus make the process of combating the disease much easier.

A model of an individual's cardiovascular system must mimic the relationship among physiological variables (i.e., heart rate, systolic and diastolic blood pressures, and breathing rate) at different physical activity levels. If a model is adapted to an individual, then it becomes a model of the physical condition of that individual. The simulator will have to be able to adapt to the features of any individual without the supervision of an expert. This calls for a neural network.

Another reason that justifies the use of ANN technology, is the ability of ANNs to provide sensor fusion which is the combining of values from several different sensors. Sensor fusion enables the ANNs to learn complex relationships among the individual sensor values, which would otherwise be lost if the values were individually analysed. In medical modelling and diagnosis, this implies that even though each sensor in a set may be sensitive only to a specific physiological variable, ANNs are capable of detecting complex medical conditions by fusing the data from the individual biomedical sensors.

This model could be used to monitor employees in hazardous environments like fire-fighters. The system could be used to determine whether firemen have recovered sufficiently from the last inhalations of smoke to be allowed to enter smoke-filled environments again.

The advantages that such a system can offer are obvious. People can be checked for heart diseases quickly and painlessly and thus detecting any disease at an early stage. Of course, the system doesn't eliminate the need for doctors since a human expert is more reliable.

Electronic noses
ANNs are used experimentally to implement electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odours in the remote surgical environment. These identified odours would then be electronically transmitted to another site where an odour generation system would recreate them. Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery.
For more information on telemedicine and telepresent surgery click here.

Pattern Recognition of Pathology Images
Pathology is an imaging technique in medicine which deals with the nature of disease (structural and functional changes in tissue). Its need of colour and high resolution makes the use of digital image technology very difficult to implement.

Pattern recognition is an idea of classifying input data into identifiable classes by use of significant feature attributes of the data (sample), where the feature attributes are extracted from a background of irrelevant detail.

Neural Networks are used in pattern recognition because of their ability to learn and to store knowledge. Because of their 'parallel' nature can achieve, ANNs can achieve very high computation rates which is vital in application like telemedicine.

Instant physician
An application developed in the mid-1980s called the "instant physician" trained an autoassociative memory neural network to store a large number of medical records, each of which includes information on symptoms, diagnosis, and treatment for a particular case. After training, the net can be presented with input consisting of a set of symptoms; it will then find the full stored pattern that represents the "best" diagnosis and treatment.

ANNs have a lot to offer to modern medicine. At the moment they are mainly used for pattern recognition using images but experiments are being done in using ANNs to model parts of the human body. The high computation rates of ANNs are vital to telemedicine which is a 'hot' research area at the moment. Neural networks will never replace human experts but they can help in screening and can be used by experts to double-check their diagnosis.

1. Artificial Neural Networks in Medicine

2. A Novel Approach to Modelling and Diagnosing the Cardiovascular System

3. Electronic Noses for Telemedicine

4. Pattern Recognition of Pathology Images

5. Neural Networks at Pacific Northwest National Laboratory