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October 10, 2019 Thursday 10:40:10 PM IST

Scientists are Wearing VR Goggles to Analyse Data


Scientists have started using virtual realty tools nto for gaming or entertainment but to analyse and comprehend data.


"In the future, a scientist might be working on their desktop, and then they could just grab a pair of virtual reality glasses, or they may even be already wearing regular glasses that enable VR, and then start manipulating their data in the same shared visual context of their actual work area," according to Santiago Lombeyda, computational scientist at the Center for Data-Driven Discovery (CD3). It is a joint partnership between Caltech and JPL, which is managed by Caltech for NASA.

The CD3 group's latest project, in collaboration with the National Cancer Institute, is to develop better tools for finding tumors in diagnostic imaging scans. To that end, they have developed a virtual reality environment, using a Vive VR headset, where a user can visualize computed tomography (CT) scans from patients and identify possible tumors. According to the Caltech and JPL researchers, the 3-D virtual environment allows radiologists to better visualize and identify potential tumors than with the standard 2-D imaging methods available now.


"The 3-D environment can really have an impact in understanding three-dimensional structures," says Lombeyda. "And that's why it applies so well to looking for tumors. By walking around the data and seeing it from all sides, you might find things you wouldn't otherwise see by just looking at 2-D slices of tissues."

The group has begun working with several radiologists to identify possible tumors in patients. The idea is to then take this VR training data and feed it into machine-learning, or artificial intelligence (AI), programs, an effort being managed by Caltech's Ashish Mahabal, lead computational and data scientist for CD3. Once those programs have learned how to better identify tumors, they could be used in the future to help medical professionals find candidate tumors for follow-up. "The better the training data, the better the machine-learning program," says Djorgovski.



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