This scientific discussion will review how emerging AI solutions that employ quantitative neuroimaging can help address some of the key challenges faced in Alzheimer’s Disease (AD) clinical trials including:
One important reason that AD trials have failed to show positive results is the inherent heterogeneity of the AD population. This has resulted in trials being underpowered due to the inclusion of patients who were not going to decline during the trial and thus were not able to demonstrate a treatment effect.
We will discuss the recent focus on improving stratification by using machine learning to predict a patient’s likely risk to decline over the period of the trial and the impact of using these models to enrich patient selection.
We will also provide an overview of the AD trials that have employed quantitative imaging as an endpoint and why providing these measures is important.
4th Global Biomedical Frontier Technology Conference
Forum 10: New Drug Clinical Development
Hall G, Suzhou Expo Center