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

I-Corps：人工智能 (AI) 脑部病变检测诊断软件

• 批准号: 2234944
• 负责人: Mark McManis
• 金额: $ 5万
• 依托单位: University of Arkansas Medical Sciences Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234944&HistoricalAwards=false
• 关键词: Corps; Artificial; Intelligence; AI; Brain

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.

这个I-Corps项目更广泛的影响/商业潜力是开发一种简单的、工作流程驱动的解决方案，用于检测局灶性皮质发育不良（FCD），具有高灵敏度和特异性。 由于许多农村医院没有癫痫专科医生，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：临床神经病学团队-使他们能够更好地评估疑似FCD的患者进行手术;医院-使他们能够提供更好的患者护理;患者-确保他们过上无创伤的生活;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。I-Corps项目基于开发基于云的软件系统，通过使用自适应深度机器学习（ML）分割磁共振成像（MRI）图像并指示疑似病变，以至少90%的准确率自动识别局灶性皮质发育不良（FCD）病变。输出将是一个MRI序列，突出显示病变，并提供一份临床医生友好的报告，说明存在FCD病变的概率，如果存在，还说明位置。FCD病变的自动检测可以改善癫痫患者的护理。FCD病变导致一种以耐药性癫痫发作为特征的癫痫。这些病变由于其特征和位置而难以检测。目前使用视觉MRI检查的护理标准错过了高达50%的病例，即使是由经验丰富的神经放射科医生雇用。这些高度专业化的医生通常只在选定的学术医疗中心或城市环境中提供。由于专家的有限性和这些病变的高度可治疗性与手术的性质，有一个显着未满足的需求，在MRI扫描中的FCD病变的有效和准确的识别。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。