Electrotechnology Applications Center


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Electrotechnology Applications Center

Electrotechnology Applications Center

Neural Networks are a form of artificial intelligence that use multiple artificial neurons, networked together to process information. This type of network has the capability to learn from patterns, and extrapolate results from data that has been previously entered into the network's knowledge base. The ability to learn makes neural network applications extremely valuable to the medical industry, particularly in the area of in-office, or online medical diagnosis. Using neural nets as a data mining solution may allow medical facilities of the future to present patients with the option of receiving online medical advice from an artificial intelligence software program.

Current Health Diagnosis Procedures

Doctors use a combination of a patient's case history and current symptoms to reach a health diagnosis when a patient is ill. In order to recognize the combination of symptoms and history that points to a particular disease, the doctor's brain accesses memory of previous patients, as well as information that has been learned from books or other doctors. A neural network has the ability to mimic this type of decision-making process, and use a knowledge base of information, and a training set of practice cases, to learn to diagnose diseases.

Artificial Neural Networks Accurately Diagnose Disease

  • "Classification and Prediction of the Progression of Thyroid-associated Ophthalmopathy by an Artificial Neural Network", published in 2002 by the National Center for Biotechnology Information.

In this study, the use of neural networks to diagnose and predict the progression of eye problems associated with thyroid disease was examined. It was determined that with appropriate information and a learning process, the neural network was able to correctly identify almost 80% of eyes as being positive or negative for thyroid associated ophthalmopathy (TAO). The network was also able to correctly predict the progression of the disease in almost 70% of the patients.