New artificial intelligence technology that uses a common CT angiography (CTA), as opposed to the more advanced imaging normally required to help identify patients who could benefit from endovascular stroke therapy (EST), is being developed at The University of Texas Health Science Center at Houston (UTHealth). Research to further develop and test the technology tool is funded through a five-year, $2.5 million grant from the National Institutes of Health (NIH).

两名UTHEADY研究人员beplay苹果手机能用吗共同创建了一种机器学习的人工智能工具，可用于评估每家照顾中风患者的医院中风 - 不仅在主要城市的大型学术医院。

"The vast majority of stroke patients don't show up at large hospitals, but in those smaller regional facilities. And most of the emphasis on screening techniques is only focused on the technologies used in those large academic centers. With this technology, we are looking to change that," said Sunil Sheth, MD, assistant professor of neurology at McGovern Medical School at UTHealth.

An ischemic stroke occurs when blood flow to a part of the brain is impeded, meaning that part of the brain is not receiving the oxygen and nutrients it needs to survive. Stroke is the fifth leading cause of death in the U.S. according to the Centers for Disease Control and Prevention and the 10th leading cause of disability.

大血管阻塞是一种脑部的一种，其中大脑的主要动脉被阻塞。在临床试验中，这些患者从EST中受益，其中将导管插入大脑以去除凝块。但是，什斯解释说，当治疗这些类型的中风时，治疗速度至关重要。

"There is a large territory of the brain that is at risk of dying with large vessel occlusion, so we need to make sure we evaluate these patients as quickly as possible so they can receive the treatment they need," he said.

Sheth set out with卢卡·吉安卡多（Luca Giancardo），UTHealth BioMedical Informatics学院精密健康中心的助理教授博士，以开发一种评估患者的更快方法。结果是一种利用脑对称性的新型深神经网络结构。使用更广泛可用的CTA，该系统可以确定大容器阻塞的存在或不存在，以及“处于危险”组织的量是否高于或低于临床中EST的患者的阈值试验。

"This is the first time a data set is being specifically collected aiming to address the lack of quality imaging available for stroke patients at smaller hospitals," Giancardo said.

Giancardo said this technology is also important to help those smaller regional hospitals determine if a patient needs to be transferred to a larger one. "This technology will allow those hospitals to screen these patients more effectively and quickly, and then be able to transfer them faster to the larger academic hospitals where EST is available," he said.

Sheth和Giancardo很高兴他们能够在这项技术上进行合作。

"We have a unique opportunity here at UTHealth since we have several institutions with experts from different areas that are able to use their different skillsets and knowledge to work together to tackle problems like this. I am very grateful for this opportunity," Giancardo said. Sheth and Giancardo are also both faculty members of theUTHealth Institute for Stroke and Cerebrovascular Disease.

使用从格兰特,Sheth和Giancardo will establish a multi-institution dataset for research in acute ischemic stroke. This data will be collected from 15 hospitals across Texas and California including both larger academic institutions like UTHealth and smaller community hospitals. The team will also develop interpretable deep-learning models to determine a patient's eligibility for EST, and deploy the technology to hospitals not involved in the creation of the models to test its validity.

"The device will learn to assess the patient based on the data models we create," Giancardo said.

Giancardo is also excited about what this technology could mean for the future of stroke care. "It is going to be able to be used in as many hospitals as possible so that many more stroke patients will be able to get the treatment they need," he said.

The study was funded by the National Institute of Neurological Disorders and Stroke, part of the NIH. (grant R01NS121154)

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