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

这个i-Corps项目的更广泛的影响/商业潜力是在手术中检测中风，并减少进行有效临床干预的时间。由于检测延迟，现有的经批准的、挽救生命的、机械血栓清除疗法并未常规用于中风。这种延迟促使人们建议使用神经监测来检测中风。目前，在高风险的心血管外科手术中，训练有素的神经科医生连续监测脑电(EEG)信号，并应用经验标准来检测脑缺血和中风。然而，这种目视监测对于监测许多手术的神经科医生来说可能是精神上要求很高的，质量上的变化，以及可扩展性的限制。尽管有EEG设备，但并不是所有的医疗机构都能使用。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救生治疗的管理。该项目的成功开发可能转化为神经监测在外科手术中的广泛采用，不仅适用于中风，也适用于脊髓和周围神经损伤。这个i-Corps项目进一步开发了一个软件系统，可以显示术中脑电(EEG)信号，并使用机器学习(ML)来检测中风并实时向监测神经科医生发出警报。具有ML功能的人工智能软件系统可以快速、准确地处理海量数据，并与临床医生共生工作。该系统依赖于使用脑电的ML模型，以及在颈动脉内膜切除术(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中风的前兆--缺血。这项裁决反映了NSF的法定使命，并通过使用基金会的智力优势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.