SBIR Phase I: AI-assisted software for fast labeling of medical tomographic images

SBIR 第一阶段：用于快速标记医学断层扫描图像的人工智能辅助软件

• 批准号: 2136669
• 负责人: Sergey Anishchenko
• 金额: $ 25.58万
• 依托单位: ALIENBYTE SCIENTIFIC SOFTWARE INC
• 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: 2022-02-15 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136669&HistoricalAwards=false
• 关键词: SBIR; Phase; AI; assisted; software

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to extract new valuable information from medical images, accelerate image interpretation by radiologists, and improve patient outcomes. The process of identifying key features in images, known as ``labeling'', is the key to improved diagnosis and management of certain conditions. The innovations proposed here will substantially lower the cost of labeled datasets, enabling access for developers of artificial intelligence (AI) algorithms and improving the use of AI in health care. This Small Business Innovation Research (SBIR) Phase I project will apply machine learning algorithms to develop a system for assisting in manual labeling of medical tomographic images. The proposed research will result in an adaptive system architecture that evolves to accelerate labeling and increase the volume of labeled data. Moreover, the research will increase labeling accuracy at the edges of anatomical structures. For instance, surgical resections for cancer treatment requires accurate labeling of the edges of abnormal tissue to ensure clean margins and minimal recurrence. Similarly, radiation therapy planning requires accurate labeling of the edges of organs at risk for safety and favorable outcomes. Due to its clinical importance, accurate manual labeling of ambiguities and sophisticated shapes is highly time-consuming. The proposed approach is differentiated from current methods by the inclusion of an additional subsystem for increasing the accuracy of edge labeling.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响是从医疗图像中提取新的有价值信息，加速放射科医生的图像解释并改善患者的结果。识别图像中的关键特征（称为``标记''）的过程是改善某些疾病诊断和管理的关键。 这里提出的创新将大大降低标记数据集的成本，从而为人工智能（AI）算法的开发商提供访问权限，并改善AI在医疗保健中的使用。这项小型企业创新研究（SBIR）I阶段项目将应用机器学习算法来开发一种系统来协助手动标记医疗层析成像图像。拟议的研究将导致自适应系统体系结构，该体系结构发展为加速标记并增加标记数据的数量。此外，该研究将提高解剖结构边缘的标记准确性。例如，用于癌症治疗的手术切除术需要准确地标记异常组织的边缘，以确保清洁边缘和最小的复发。同样，放射治疗计划需要准确地标记有安全和有利结果的器官的边缘。由于其临床重要性，歧义性和复杂形状的准确手动标记非常耗时。提出的方法与当前方法通过包括一个额外的子系统，以提高边缘标签的准确性。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子和更广泛的影响审查标准，认为值得通过评估来支持。