Imagine having a high-tech machine that can predict the future, like a crystal ball! Well, scientists have created a machine learning-based tool called the AD-resemblance atrophy index (AD-RAI) that can assess the risk of developing Alzheimer’s disease (AD). This tool measures the shrinkage of specific brain regions to identify AD and predict its progression. Researchers tested the AD-RAI’s reliability by analyzing brain scans of individuals with AD and non-demented controls over a two-year period. The results were impressive! The AD-RAI showed excellent consistency when repeated on the same day and after a two-week interval. It also performed better than other methods in distinguishing AD from non-AD cases. Furthermore, higher baseline AD-RAI values were associated with lower cognitive scores and a faster decline in cognitive abilities over time. In other words, the AD-RAI serves as a potential biomarker for diagnosing AD and predicting cognitive decline.

This ground-breaking research opens up exciting possibilities for early detection and personalized treatment of Alzheimer’s disease. By using the AD-RAI, medical professionals can intervene sooner, potentially improving outcomes for patients and their families. More studies are needed to fully understand how accurately the AD-RAI can predict future cognitive decline and how it compares to other diagnostic tools. To learn more about this innovative brain atrophy marker and its potential implications for Alzheimer’s research, check out the full article!

BackgroundAutomated tools for characterising dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer’s disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction.MethodsAge- and sex-matched 44 probable AD (Age: 69.13 ± 7.13; MMSE: 27–30) and 22 non-demented control (Age: 69.38 ± 7.21; MMSE: 27–30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD) dataset. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong’s test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed.ResultsAD-RAI had excellent same-day repeatability and excellent 2-week reproducibility. AD-RAI’s AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients.ConclusionsThe AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.

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