WARF TECHNOLOGIES

Recent Alzheimer’s Disease (AD) clinical trials have faced challenges at the mild-to-moderate dementia stage. There’s consensus that trials should focus on AD stage, such as MCI or the presymptomatic stage. However, the heterogeneous nature of MCI makes it difficult to identify probably beneficiaries of treatment. 

To address this, a multimodal imaging marker combines [F-18] fluorodeoxyglucose PET, amyloid frobetapir PET, and structural MRI with a deep learning algorithm for a trial enrichment criterion. It reduces sample estimates and leads to smaller trials with high statistical power compared to existing methods.

• Select MCIs likely to decline, improving trial efficiency
• Identifies patients who benefit most from treatment
• Tailors treatment to individual profiles

• Fewer participants needed for trials
• Increased likelihood of detecting treatment effects
• Promising approach to personalized care

In a study focused on evaluating the effectiveness of deep learning in predicting cognitive decline and neural changes in individuals with mild cognitive impairment (MCI) related to Alzheimer’s disease (AD), the algorithm was applied to PET and MRI imaging data. Results showed that the algorithm successfully identified low-risk subjects who were less likely to experience significant cognitive decline. By excluding these low-risk individuals, the study reduced the sample size needed for clinical trials.
 

For More Information About the Inventors

• Vikas Singh
• Sterling Johnson
• Ozioma Okonkwo

Publications

• Ithapu et al. Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment. Alzheimers Dement. 2015 Dec;11(12):1489-1499. doi: 10.1016/j.jalz.2015.01.010. Epub 2015 Jun 18. PMID: 26093156.

Tech Fields

• Education & Training : Medical & health
• Medical Devices : Diagnostics & monitoring tools