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

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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.