Artificial Intelligence May Aid in Alzheimer’s Diagnosis

July 06, 2016

With automated methods, age- and sex-adjusted arterial spin labeling (ASL) perfusion maps can be used to classify and predict diagnosis of Alzheimer’s disease (AD), conversion of mild cognitive impairment (MCI) diagnosis to AD, stable MCI, and subjective cognitive decline (SCD) with good to excellent accuracy and area under the receiver-operating characteristics curve (AUC) values, according to a new study published in Radiology.

Lyduine E. Collij, BSc, of VU University Medical Centre in Amsterdam, the Netherlands, and colleagues acquired pseudocontinuous 3.0-T ASL images in 100 patients with probable AD; 60 patients with MCI — of these, 12 remained stable, 12 were converted to a diagnosis of AD and 36 had no follow-up; 100 subjects with SCD; and 26 healthy control subjects. The AD, MCI and SCD groups were divided into a sex- and age-matched training set and an independent prediction set.

Single-subject diagnosis in the prediction set by using the discrimination maps yielded excellent performance for AD versus SCD, good performance for AD versus MCI, and poor performance for MCI versus SCD. Application of the AD versus SCD discrimination map for prediction of MCI subgroups resulted in good performance for patients with MCI diagnosis converted to AD versus subjects with SCD and fair performance for patients with MCI diagnosis converted to AD versus those with stable MCI.

“Our results support the way automated classification can facilitate and possibly improve diagnosis, specifically in centers without experienced (neuro) radiologists. In addition, automated classification may be applicable for screening purposes, considering the high prevalence of AD,” the authors write.

The classifiers can be represented as discrimination maps
Figure 1. The classifiers can be represented as discrimination maps, where a red color indicates that the intensity at that location contributes to the likelihood of the images belonging to the more advanced stage, and a blue color to the likelihood of belonging to the less advanced stage. Weights are shown inside the mask that resulted in the highest accuracies for each classification: A: Alzheimer's disease (AD) vs. subjective cognitive decline (SCD); B: AD vs. mild cognitive impairment (MCI); C: MCI vs. SCD.
Discrimination maps for classifying MCI subgroups
Figure 2. Discrimination maps for classifying MCI subgroups, inside the masks that resulted in the highest accuracies. A: between patients with MCI that converted to AD (MCIc) and subjects with SCD; B: between MCIc and patients with MCI that remained stable (MCIs).

Web Extras

Editor's Picks

New Video Series Helps Patients Understand Their Radiology Reports
Providing Imaging Assistance for the Underserved in Your Community
RSNA and GE HealthCare Bring Advanced Mammography Technology to Tanzania to Improve Access and Help Address Breast Cancer Mortality Rate
GPT-4 Matches Radiologists in Detecting Errors in Radiology Reports 
News in Brief

Annual meeting
Education and funding
Journal highlights
Member updates
R&E Foundation
Radiology in public focus