A computer-based expert system can diagnose Alzheimer's disease with an accuracy comparable to experienced nuclear medicine physicians, according to a study presented at the Society of Nuclear Medicine meeting in June.
A computer-based expert system can diagnose Alzheimer's disease with an accuracy comparable to 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, according to coauthor Dr. Peter Bartenstein, chair of nuclear medicine at Gutenberg University Mainz in Germany.
Bartenstein and colleagues used 3D standard surface projections of stereotactically normalized brain PET scans and a data set of standardized regions of interest. These were projected in frontal, central, parietal, temporal, and occipital areas of the brain as the basis for an automated expert system.
Two expert readers established a set of rules for diagnosis by comparing the 3D surface projections with 20 normal controls. The rules were used 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. They compared the automated system results with reads done by three experts who had been blinded to all other imaging or clinical data.
The concordance between the automated system and the nuclear medicine experts for all data sets had a kappa value of 0.76 to 0.83, with a kappa value of greater than 0.7 indicating satisfactory congruence.
The use of the system did not significantly increase the time needed for analysis, which took less than 15 minutes, according to Bartenstein.
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.
Future enhancements to the system could include the implementation of artificial intelligence so that the program can improve on the quality of its decisions. This implementation could also extend the system to identify 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."
For more information, visit Diagnostic Imaging's PACSweb section.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.