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

该 I-Corps 项目更广泛的影响/商业潜力是在手术期间检测中风并减少有效临床干预的时间。由于检测延迟，现有经批准的救生机械血栓清除疗法并未常规用于中风治疗。这种延迟促使人们建议使用神经监测来检测中风。目前，在高风险心血管手术中，训练有素的神经科医生持续目视监测脑电图 (EEG) 信号，并应用经验标准来检测脑缺血和中风。然而，对于监测许多手术的神经科医生来说，这种视觉监测可能对精神要求很高，质量不稳定，并且限制了可扩展性。 尽管有脑电图设备，但并非所有医疗机构都能使用。显然需要可扩展的解决方案来支持神经科医生改进中风检测和及时实施挽救生命的治疗。 该项目的成功开发可能意味着神经监测在中风手术以及脊髓和周围神经损伤手术中的广泛采用。该 I-Corps 项目进一步开发了一个软件系统，可以显示术中脑电图 (EEG) 信号，并使用机器学习 (ML) 来检测中风并实时向监测神经科医生发出警报。具有机器学习功能的人工智能软件系统可以快速、准确地有意义地处理大量数据，并与临床医生共生。 该系统依赖于使用 EEG 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 模型还可用于检测缺血（中风的先兆）。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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.