SBMI Assistant Professor Luca Giancardo, PhD has been awarded one of six grants from Baylor College of Medicine's (BCM) Translational Research Institute for Space Health (TRISH). Each recipient’s project focuses on advancing biomedical and health research in deep space. In a BCM news release published in November, TRISH stated that the “projects aim to develop novel solutions to some of NASA’s highest priority risks to human health and performance during deep space exploration missions.
An untreated stroke event would be destructive for an astronaut during a deep space exploration mission. Increased cerebrovascular disease risk has been documented after prolonged exposures to ionizing radiations on Earth. Astronauts on deep space missions will be exposed to galactic cosmic rays and solar particles for 30 months, which can lead to accelerated vascular injury likely increasing their risk of stroke.
The project aims to develop new technological approaches to detect and differentiate various stroke types, which can be very difficult in space. On Earth, these strokes are quickly identified by Computer Tomography (CT) or Magnetic Resonance Imaging (MRI) scan. However, these brain imaging capabilities are not available in deep space and alternative, robust means of diagnosing and classifying strokes are needed. “Our ultimate objective is to develop a machine learning-based algorithm to detect ongoing acute stroke and the stroke type using non-invasive retinal imaging devices, which will be available in deep space missions,” Giancardo stated.
The project itself is interdisciplinary in nature. Giancardo is serving as PI for the team project, along with two Co-Investigators; Vascular and Interventional Neurologist and Assistant Professor at McGovern Medical School, Sunil Sheth, MD and Ophthalmologist and Assistant Professor at BCM, Roomasa Channa, MD. Channa, who is familiar with Giancardo’s previous algorithm-centric research as it relates to developing retina image analysis, introduced him to the TRISH grant opportunity. Giancardo noted that “while attending a seminar, I learned that strokes were one of the issues astronauts face during deep space missions so I decided to further explore this matter.
This research team is uniquely positioned to pursue this type of research. Beyond expertise in machine learning algorithm development for stroke brain imaging and retina imaging, the team has the support of the Institute for Stroke and Cerebrovascular Diseases at UTHealth. The institute is at the forefront of stroke research and has access to data from a large population. Furthermore, it was one of the first stroke centers established in the world and the first Stroke Center established in Houston. Giancardo is a faculty member in SBMI’s Center for Precision Health.
The research team aims to adapt an automated interpretable image-based deep learning algorithm to both identify stroke and stroke type from retinal vascular images. This would enable an automated life-saving tool that is usable during deep space exploration missions. “If successful, we would be able to create a system that will not only impact the deep space mission but it will be extremely useful for stroke management when brain imaging is not available,” says Giancardo.
The grant is worth just under $800,000 and includes additional support from UTHealth. Giancardo received two years of funding support from TRISH.
published on 12/02/2019 at 09:20 a.m.