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(PSYCHIATRIC TIMES) - As more physicians begin to use FDG-PET for diagnosing Alzheimer's disease, the demand for automated software systems that help interpret complex metabolic scans is increasing. Several new automated expert systems have been developed that can improve diagnostic accuracy and help assess risk for the disease.

The Centers for Medicare and Medicaid Services' decision last year to provide reimbursement for PET imaging of suspected Alzheimer's patients opened the field to physicians with varying levels of experience.

"We're now seeing substantial increases in demand for software programs that can help physicians with these complicated reads, especially in places that haven't been doing these types of diagnoses before," said Dr. Daniel Silverman, head of neuronuclear imaging at the University of California, Los Angeles Medical Center.

Silverman and colleagues developed the NeuroQ software program, which received FDA approval last year and is produced commercially by Atlanta-based Syntermed. The program quantifies the amount of activity in each of 240 regions of a PET scan and identifies abnormal areas. It returns information in a color-coded display in which blue represents normal metabolic activity, red represents abnormal, and violet indicates in-between activity.

Researchers at Gutenberg University Mainz in Germany have developed a computer-based expert system that can diagnose Alzheimer's disease with an accuracy comparable to that of experienced nuclear medicine physicians, according to a study presented at the Society of Nuclear Medicine meeting in June.

Nuclear medicine physicians often look for a typical pattern of impaired cerebral glucose metabolism in determining this diagnosis, said coauthor Dr. Peter Bartenstein, chair of nuclear medicine at Gutenberg.

Bartenstein and colleagues used 3D standard surface projections of stereotactically normalized PET brain scans and a data set of standardized regions of interest. These were projected on frontal, central, parietal, temporal, and occipital areas of the brain as the basis for an automated system. Two expert readers established a set of rules for diagnosis by comparing the 3D surface projections with 20 normal controls. They used the rules to develop an automated system that would generate a straightforward AD or non-AD diagnosis.

The researchers tested the system on 150 PET data sets, comparing the automated system results with reads done by three experts who had been blinded to all other imaging or clinical data. Concordance between the automated system and the nuclear medicine experts for all data sets had a kappa value of 0.76 to 0.83. A kappa value greater than 0.7 indicated satisfactory congruence.

The use of the system did not significantly increase the time needed for analysis, which took less than 15 minutes, Bartenstein said.

One major application for the system could be training physicians to diagnose AD. Inexperienced readers reported that the system was both a welcome aid and a learning tool, he said.

Ongoing enhancements could include artificial intelligence that would improve the quality of the program's decisions. This implementation might also extend the system's capability to include identification of specific patterns in other dementia disorders such as frontotemporal or Lewy body dementia.

"Ultimately, the final diagnosis of the patient's PET scan should not be based on the results provided by the program alone," Bartenstein said. "It should be used mainly for self-evaluation. In difficult cases, it could be used to support the decision the physician has already made."

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