Multimodal deep learning algorithm as a tool for Dementia clinical trial patient disease screening

Oregon Convention Center 

Dementia is a complex disease due to various etiologies. New multimodal deep learning algorithms were developed to improve the diagnosis of dementia into different categories of normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD).
One of the core difficulties in implementing Dementia clinical trials, especially the AD trials lies in the diagnostic ambiguity of Alzheimer's, where symptomatic overlap with other cognitive disorders often leads to misdiagnosis. Dementia clinical trials usually have high screen failure rates and burden for the sponsor for the manual inclusion screening verification.
In our work, we explore the use of this multimodal deep learning algorithm as a tool for the clinical trial patient disease screening verification to reduce the cost of the clinical study while improving the quality. We will present the accuracy assessment of the deep learning algorithm compared to the neurologist assessment based on the sensitivity, specificity, PPV and NPV in the real-world clinical trial setting. We will explore the optimal set of input variables used for the algorithm to balance the accuracy and cost and time of the medical exams.

multimodal deep learning algorithm

Dementia clinical trial

disease screening

sensitivity, specificity, PPV and NPV

real world 

Biopharmaceutical Section