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t m c » p u l s e | d e c 2 0 1 9/JA N 2 02 0 26 undergo standard imaging called a CT angiogram along with a CT perfusion, which is used to decide if someone would benefit from an endovascular procedure to remove a blood clot. "We took patients who had both—the CT angiogram, which can be done at any hospital, and the CT perfusion imaging—and then we sent that into Dr. Giancardo's software. What that essentially did is trained the algorithm to take the CT images and to generate the type of output that the CT perfusion was telling us," Sheth said. "Then we tested it. Here are a bunch of patients that you've never seen before. How good are you at predict- ing what the CT perfusion is going to say? And that's what we did in our paper and showed that it did a very good job." The study included more than 200 images from a single hospital. The technology hasn't been imple- mented clinically. "The main benefit to a patient is that a lot of hospitals from other nations have the basic imaging, but not more advanced capabilities. So, the approach is that they could get the same information with the infrastructure that is already there," Giancardo said. Replacing the radiologist? The AI disruption in radiology may be predictive of what's to come in other areas of medicine. But does AI mean that the demand for radiolo- gists will decline? Walser, UTMB's radiology chair- man, thinks so. "There will be fewer of us prob- ably needed," he said. "Radiologists are going to become more the man- agers of the data rather than the creators of the diagnoses." Sheth, the UTHealth neurologist, views AI and radiology as "decision support" for the specialists. "I don't think we will ever be at the point where we can say 'Do x, y and z to this patient because Dr. Giancardo's software told us to.' This is going to be something that will help all of the physicians taking severe disability to sometimes almost completely back to normal," said Sheth, who practices as a vascular neurologist with Memorial Hermann Health System. "The chal- lenge is that we don't know who will benefit from the treatment." Finding out depends on advanced imaging techniques that are not available at most community hospitals, the first stop for the vast majority of stroke patients. "What we are trying to do, in using Dr. Giancardo's software, is to see if we could generate the same type of results that we get with advanced imaging techniques but with the type of imaging that we already do routinely in stroke in the less-advanced centers," Sheth said. "The purpose of this software is that—no matter what hospital you show up at—you can get the same type of advanced evaluation and all of the information you need to make a treatment decision." At Memorial Hermann-Texas Medical Center, patients have access to advanced technology and Researchers at The University of Texas Health Science Center at Houston (UTHealth) have demon- strated that distinction by building an AI platform called DeepSymNet that has been trained to evaluate data from patients who suffered strokes or had similar symptoms. A team including Sunil Sheth, M.D., an assistant profes- sor of neurology at UTHealth's McGovern Medical School, and Luca Giancardo, Ph.D., an assistant professor at UTHealth's School of Biomedical Informatics, created an algorithm to assist doctors outside of major stroke treatment facilities with diagnoses. The work was pub- lished online in September in the journal Stroke. The project started because of difficulties identifying patients who could benefit from an endovascular procedure that opens blocked blood vessels in the brain, a common cause of stroke. "It's one of the most effec- tive treatments we can render to patients. It takes them from having