I-Corps: Algorithm to detect stroke during cardiovascular surgery and reduce the time to effective clinical intervention

I-Corps项目更广泛的影响/商业潜力是在手术中检测中风，减少有效临床干预的时间。可获得批准的挽救生命的机械凝块清除疗法由于检测延迟而不能常规用于中风。这种延迟促使人们推荐神经监测来检测中风。目前，在高危心血管手术中，由训练有素的神经科医生连续视觉监测脑电图（EEG）信号，并应用经验标准检测脑缺血和中风。然而，这种视觉监测可能对精神要求很高，质量参差不齐，并且限制了监测许多手术的神经科医生的可扩展性。尽管有脑电图设备，但并非所有医疗机构都能普遍使用。显然需要可扩展的解决方案，以支持神经科医生改进中风检测和及时的救生治疗管理。这个项目的成功发展可能转化为广泛采用神经监测手术不仅中风，而且脊髓和周围神经损伤。I-Corps项目进一步开发了一个软件系统，该系统可以显示术中脑电图（EEG）信号，并使用机器学习（ML）来检测中风并实时提醒监测神经科医生。具有机器学习功能的人工智能软件系统可以快速、准确地处理大量数据，并与临床医生协同工作。该系统依赖于使用脑电图的ML模型，以及颈动脉内膜切除术（CEA）期间收集的临床和麻醉数据。最初的ML模型也可以用来检测缺血，这是中风的前兆。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is to detect stroke during surgery and reduce the time to effective clinical interventions. Available approved, lifesaving, mechanical clot removal therapy is not routinely administered for stroke due to delays in detection. This delay has prompted the recommendation of neuromonitoring to detect strokes. Currently, in high-risk cardiovascular surgery, a trained neurologist visually monitors electroencephalogram (EEG) signals continuously and applies empirical criteria to detect cerebral ischemia and stroke. However, such visual monitoring can be mentally demanding, variable in quality, and limiting in scalability for the neurologist who monitors many surgeries. Despite the availability of EEG devices, they are not universally available at all medical institutions. There is a clear need for scalable solutions that can support the neurologist to improve stroke detection and the administration of timely lifesaving therapies. Successful development of this project may translate to widespread adoption of neuromonitoring in surgery for not only stroke but spinal cord and peripheral nerve injuries.This I-Corps project further develops a software system that can display intraoperative electroencephalogram (EEG) signals and use machine learning (ML) to detect stroke and alert the monitoring neurologist in real-time. An artificial intelligence software system with ML capabilities can meaningfully process massive sets of data quickly, accurately, and symbiotically work alongside clinicians. The system relies on ML models using EEG, and clinical and anesthesia data collected during carotid endarterectomy (CEA). The initial ML models can also be used to detect ischemia, a precursor of stroke.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.