Rashidi awarded NIA R01 grant to study automated pre-operative cognitive risk detection using Artificial Intelligence Techniques

Dr. Parisa Rashidi, UF BME assistant professor, and collaborators were recently awarded an R01 grant from the National Institute on Aging (NIA) for their work entitled, “PRECEDE: PREsurgical Cognitive Evaluation via Digital clockfacE drawing.”
Pre-operative cognitive abilities and increasing older age are important predictors for negative post-operative outcomes. Currently, healthcare systems lack an efficient mechanism for systematically screening cognition in older adults within the perioperative setting. Barriers to routine screening include a limited appreciation for the subtlety and pervasiveness of cognitive impairment, the multiple subtypes of cognitive impairment, the impact of pre-surgery cognition on post-operative outcomes, and implementation of a widely-validated time-efficient screening tool. In response to these limitations, the current investigation will examine the novel implementation of a classic cognitive screening test for cognitive impairment: the Clock Drawing Test (CDT).
Using advanced machine-learning, the team plans to modernize the classic clock drawing task into the digital Clock Drawing Task. It will utilize a digital pen that records its position on the paper 80 times a second with a spatial accuracy of 2 one-thousandths of an inch, complemented by accelerometry measurements. With expertise from Dr. Rashidi (Co-I; UF) and MIT researchers, the team will apply deep learning neural network approaches to uncover latent clock features important in distinguishing cognitive impairment and the changes between preoperative and postoperative drawings.
This project will innovatively leverage a brief but informative test to examine the subtlety of pre-surgery cognition within an extremely large number of older individuals screened pre-surgically within an academic tertiary medical center. It is expected that the project will guide automated pre-operative cognitive risk detection and intervention timing.
Congratulations, Dr. Rashidi!