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.

这个小企业创新研究（SBIR）第一阶段项目的广泛影响是从医学图像中提取新的有价值的信息，加速放射科医生的图像解释，并改善患者的治疗效果。识别图像中的关键特征的过程，称为“标记”，是改进某些疾病的诊断和管理的关键。这里提出的创新将大大降低标记数据集的成本，使人工智能（AI）算法的开发人员能够访问，并改善人工智能在医疗保健中的使用。这个小企业创新研究（SBIR）第一阶段项目将应用机器学习算法来开发一个系统，以协助手动标记医学断层扫描图像。提出的研究将导致自适应系统架构的发展，以加速标记和增加标记数据的量。此外，该研究将提高在解剖结构边缘的标记精度。例如，治疗癌症的手术切除需要准确标记异常组织的边缘，以确保边缘清洁和最小的复发。同样，放射治疗计划需要准确标记有安全风险的器官边缘和有利的结果。由于其临床重要性，准确的手工标记歧义和复杂的形状是非常耗时的。该方法与现有方法的不同之处在于，它包含了一个额外的子系统，用于提高边缘标记的准确性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。