I-Corps: Artificial Intelligence (AI) Brain Lesion Detection Diagnostic Software

这个I-Corps项目的更广泛的影响/商业潜力是开发一种简单的、工作流驱动的解决方案，用于检测局灶性皮质发育不良(FCD)，具有高度的敏感性和特异性。由于许多农村医院没有癫痫专家，拟议的基于云的解决方案可以将诊断工具纳入患者的护理计划。这项技术的商业化可以使：临床神经科团队--使他们能够更好地评估疑似FCD患者进行手术；医院--使他们能够提供更好的患者护理；患者--确保他们过上没有癫痫发作的生活；以及科学家、研究人员和生物医学工程师--使他们能够使用人工智能来检测具有挑战性的医疗条件，而不仅仅限于FCD。该项目结合了生物学、医学、人工智能(AI)技术和商业。这个I-Corps项目基于基于云的软件系统的开发，通过使用自适应深度机器学习(ML)分割磁共振成像(MRI)图像并指示可疑病变，以至少90%的准确率自动识别局灶性皮质发育不良(FCD)病变。输出将是突出显示病变的MRI序列，以及临床医生友好的报告，指出存在FCD病变的可能性，如果存在，则显示位置。FCD病变的自动检测可能会改善癫痫患者的护理。FCD损害会导致一种以药物抵抗发作为特征的癫痫形式。由于其特点和位置，这些病变很难被发现。目前使用视觉MRI检查的护理标准遗漏了高达50%的病例，即使是由经验丰富的神经放射科医生雇用也是如此。这些高度专业化的医生通常只在特定的学术医疗中心或城市环境中提供。由于专家的有限，以及这些病变通过手术高度可治疗的性质，在MRI扫描中高效和准确地识别FCD病变的需求仍未得到满足。这一奖项反映了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 the development of a simple, workflow-driven solution for detecting focal cortical dysplasia (FCD) with high sensitivity and specificity. As many rural hospitals do not have epilepsy specialists, the proposed cloud-based solution can bring the diagnostic tool into their patients’ care plan. Commercialization of this technology may enable: clinical neurology teams - enabling them to better evaluate patients with suspected FCD for surgery; hospitals - allowing them to provide better patient care; patients - ensuring they live seizure-free lives; and scientists, researchers, and biomedical engineers - enabling them to use artificial intelligence to detect challenging medical conditions not limited to FCD. The project combines biology, medicine, artificial intelligence (AI) technology, and business.This I-Corps project is based on the development of a cloud-based software system to automatically identify focal cortical dysplasia (FCD) lesions with at least 90% accuracy by using adaptive deep machine learning (ML) to segment magnetic resonance imaging (MRI) images and indicate suspected lesions. The output will be an MRI sequence with lesions highlighted and a clinician-friendly report indicating the probability that a FCD lesion is present, and if so, the location. Automated detection of FCD lesions may improve the care of epileptic patients. FCD lesions result in a form of epilepsy characterized by medication-resistant seizures. These lesions are difficult to detect due to their characteristics and location. The current standard of care using visual MRI inspection misses up to 50% of cases, even when employed by experienced neuroradiologists. These highly specialized physicians are usually only available in select academic medical centers or urban environments. Because of the limited availability of specialists and the highly treatable nature of these lesions with surgery, there is a significant unmet need for efficient and accurate identification of FCD lesions in MRI scans.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.