PFI: AIR-TT: Prototype Scale-up for Traumatic Pelvic and Abdominal Injury Decision Support System (DSS)

PFI：AIR-TT：创伤性骨盆和腹部损伤决策支持系统 (DSS) 的原型放大

• 批准号: 1500124
• 负责人: Kayvan Najarian
• 金额: $ 20万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1500124&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Prototype; Scale

This PFI:AIR Technology Translation project focuses on translating and accelerating commercialization of a patented Traumatic Pelvic and Abdominal Injury Decision Support System (DSS) technology. This will enable physicians to quickly and accurately extract complex patient data from all relevant biomedical images, (e.g., CT scans with hundreds of image slices) trauma scores, diagnoses, treatments, demographics, and injury specifics for each patient?while integrating and analyzing the information to generate prediction, warning, and treatment recommendations at every stage of patient care. This project is important not only to help decrease medical complications and increase survival, but also to optimize resource utilization- a key to reducing the approximately $60B medical cost each year for treating complications in pelvic and abdominal trauma cases. The Traumatic Pelvic and Abdominal Injury DSS technology has the following unique capabilities which provide competitive advantages when compared to the existing state of the art for DSS tools: 1) it segments and assesses damage to major abdominal organs; 2) it provides recommendations and predictions for several specific, complex clinical decisions; 3) it is fully automated and does not require an expert?s supervision in analyzing patient data- providing an easier-to-use software interface and potentially providing higher accuracy in pertinent recommendations for trauma patient care. If the algorithms and software are successfully validated through this project, a licensing pathway has been identified to commercialize the DSS software.Clinical decision making shows its true complexity when one is trying to quickly and accurately integrate complex types of patient data in an emergency setting. This project addresses several technology gaps as it translates from research discovery toward commercial application. Existing "semi-automated" systems use only a portion of patient data and do not analyze detailed information contained in digital images to create recommendations; conversely, current image processing technologies designed mainly to assist in analysis of CTs (or other images) are not optimized to address the needs of trauma and/or DSS tools. This project will refine and scale up the prototype, validate its clinical use, and accelerate its commercialization to assist clinicians in traumatic pelvic and abdominal injury cases. Key technical objectives are to: 1) expand the organ segmentation software module (now covering only the spleen) to include the liver, kidneys, and pancreas; 2) enhance the hemorrhage detection algorithms to find bleeding close to bones; 3) further validate and improve the system using a larger and more comprehensive dataset; and 4) rewrite the graphical user interface to match requirements for the prototype and validate its effectiveness and ease of use by clinicians. Key computational methods generated by this project include automated image processing algorithms and machine learning methods to: 1) assess a CT scan for bone fracture(s) and hemorrhage and measure their sizes; 2) segment more major organs, identify damage, and quantitatively assess level of injury; and 3) predict outcomes (survival, number of ICU days, home vs. rehab, etc.) and form recommendations for care givers at each step of the treatment. The graduate student involved in this project will gain experience in innovation and technology translation towards commercialization through development of the DDS tool, testing and validating the algorithms, and working closing with the project team, clinicians, business developers, tech transfer professionals, and a potential licensee to commercialize the technology as a viable product.

这个PFI：AIR技术翻译项目的重点是翻译和加速专利的创伤性骨盆和腹部损伤决策支持系统（DSS）技术的商业化。这将使医生能够快速准确地从所有相关的生物医学图像中提取复杂的患者数据，（例如，CT扫描与数百个图像切片）创伤评分，诊断，治疗，人口统计学，和每个病人的伤害细节？同时整合和分析信息以在患者护理的每个阶段产生预测、警告和治疗建议。该项目不仅有助于减少医疗并发症和提高生存率，而且还可以优化资源利用-这是减少每年用于治疗骨盆和腹部创伤并发症的约600亿美元医疗成本的关键。创伤性骨盆和腹部损伤DSS技术具有以下独特的功能，与DSS工具的现有最新技术相比，这些功能具有竞争优势：1）它分割并评估主要腹部器官的损伤; 2）它为几个特定的复杂临床决策提供建议和预测; 3）它是完全自动化的，不需要专家？在分析患者数据时，提供了一个用户使用的软件界面，并可能在创伤患者护理的相关建议中提供更高的准确性。如果算法和软件通过该项目成功验证，则已确定将DSS软件商业化的许可途径。临床决策在紧急情况下试图快速准确地整合复杂类型的患者数据时显示出其真正的复杂性。该项目解决了从研究发现到商业应用的几个技术差距。现有的“半自动化”系统仅使用患者数据的一部分，并且不分析数字图像中包含的详细信息以创建建议;相反，主要设计用于辅助CT（或其他图像）分析的当前图像处理技术未被优化以解决创伤和/或DSS工具的需求。该项目将完善和扩大原型，验证其临床用途，并加速其商业化，以帮助临床医生治疗创伤性骨盆和腹部损伤病例。主要技术目标是：1）扩展器官分割软件模块2）增强出血检测算法以发现靠近骨骼的出血; 3）使用更大和更全面的数据集进一步验证和改进系统;以及4）重写图形用户界面以匹配原型的需求并验证其有效性和临床医生使用的容易性。该项目生成的关键计算方法包括自动图像处理算法和机器学习方法，用于：1）评估骨折和出血的CT扫描并测量其大小; 2）分割更多主要器官，识别损伤并定量评估损伤程度; 3）预测结果（生存率，ICU天数，家庭与康复等）。并在治疗的每一步为护理人员提供建议。参与该项目的研究生将通过开发DDS工具，测试和验证算法，并与项目团队，临床医生，业务开发人员，技术转让专业人员和潜在的被许可人合作，将技术商业化为可行的产品，从而获得创新和技术转化为商业化的经验。

项目成果

Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours

• DOI: 10.1109/embc.2018.8512182
• 发表时间: 2018-07
• 期刊: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
• 作者: Alexander Wood;S. Soroushmehr;Negar Farzaneh;D. Fessell;Kevin Ward;Jonathan Gryak;Delaram Kahrobaei-;K. Najarian

Brain Hematoma Segmentation Using Active Learning and an Active Contour Model

• DOI: 10.1007/978-3-030-17935-9_35
• 发表时间: 2019-05
• 作者: Heming Yao;C. Williamson;Jonathan Gryak;K. Najarian