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

这个PFI：空气技术翻译项目专注于翻译和加速创伤性骨盆和腹部损伤决策支持系统(DSS)专利技术的商业化。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，具有数百个图像切片的CT扫描)每个患者的创伤评分、诊断、治疗、人口统计和伤害细节-同时集成和分析信息，以在患者护理的每个阶段生成预测、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存活率，而且还有助于优化资源利用--这是降低每年用于治疗盆腔和腹部创伤并发症的约600亿美元医疗成本的关键。与现有最先进的诊断支持系统工具相比，创伤性骨盆和腹部损伤诊断支持系统技术具有以下独特的功能和竞争优势：1)它对主要腹部器官的损伤进行分割和评估；2)它为几个具体、复杂的临床决策提供建议和预测；3)它是全自动化的，不需要专家监督？S在分析患者数据方面提供了一个更易于使用的软件界面，并有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的成功验证，就已经确定了将DSS软件商业化的许可途径。当一个人试图在紧急情况下快速而准确地集成复杂类型的患者数据时，临床决策显示出其真正的复杂性。该项目在从研究发现转化为商业应用的过程中解决了几个技术差距。现有的“半自动”系统仅使用一部分患者数据，并不分析数字图像中包含的详细信息以创建建议；相反，主要用于辅助分析CT(或其他图像)的当前图像处理技术没有针对创伤和/或DSS工具的需求进行优化。该项目将改进和扩大原型，验证其临床应用，并加快其商业化进程，以帮助临床医生处理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(现在仅覆盖脾)，以包括肝脏、肾脏和胰腺；2)增强出血检测算法，以发现靠近骨骼的出血；3)使用更大、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，以满足原型的要求，并验证其有效性和临床医生的易用性。该项目产生的关键计算方法包括自动图像处理算法和机器学习方法，以：1)评估CT扫描是否有骨折(S)和出血并测量其大小；2)分割更多的主要器官，识别损伤，并定量评估损伤程度；以及3)预测结果(存活、重症监护天数、家庭与康复，等等)。并在治疗的每一步为照顾者提供建议。参与这个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、技术转移专业人员和潜在的被许可人密切合作，将技术作为可行的产品进行商业化，从而获得创新和技术转化为商业化的经验。

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.