By Anton Loukianov, Machine Learning Scientist

We are excited to announce the release of our latest Digital Twin Generator (DTG) in frontotemporal dementia (FTD). This FTD DTG 1.0 builds upon the existing base architecture employed in our DTGs for Alzheimer’s disease and amyotrophic lateral sclerosis.

FTD is a group of related brain disorders caused by degeneration of the frontal and temporal lobes of the brain. It is particularly challenging to diagnose because it can present itself in a variety of ways: difficulty speaking, changes in mobility, or altered mood and behavior. Recently, the condition gained attention when actor Bruce Willis announced that he was retiring from acting due to communication issues stemming from the disease. There are currently no treatments to slow or stop the onset of symptoms and no cure.

Although there are many promising therapies in development, the rarity and complex diagnosis of FTD has made recruiting participants for clinical studies difficult. Recruitment challenges are a surprisingly common reason for high failure rates and long development timelines in clinical trials, and we believe this is a place where novel AI-based approaches can make a huge difference.

FTD DTG 1.0 demonstrates strong performance on predicting common clinical endpoints used in FTD trials, a key component of our approach in reducing the number of participants required for clinical trials while maintaining power. Clinical trials incorporating our AI-powered digital twins enable more participants to receive the experimental treatment.

For more detailed information on our FTG DTG 1.0, including training data, a full list of endpoints, and performance metrics, see our CD DTG 1.0 specification sheet on our website.

FTD profoundly impacts those diagnosed with the condition, as well as their families and caretakers. By releasing a DTG for FTD,  we hope to accelerate the development of life-changing treatments and speed up progress in combating this debilitating disease.