Clinical Decision Support for Assessing Pulmonary Embolism using Machine Learning

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Project Summary/Abstract Minnesota HealthSolutions (MHS) proposes a Phase II project to develop and validate a software product capable of automatically detecting and staging pulmonary embolisms (PEs) using clinically routine pulmonary CT angiograms (CTAs). The proposed system will combine state-of-the-art machine learning methods and the clinical expertise at Duke University into a system that integrates seamlessly into the Radiology workflow and standard patient care path to improve the treatment decisions of physicians in the emergency department. Pulmonary embolism is the third most common cause of death in hospital patients with an estimated incidence of 1 per 1,000 patients. CTAs are routinely used to detect PE today; however, there is significant variability in the detection rate among radiologists using CTA. Furthermore, despite the strong evidence that the RV/LV ratio is an important clinical biomarker it is rarely measured quantitatively in practice. A successful completion of this project would provide a workflow-integrated tool capable of faster PE detection and more accurate staging of right heart strain to guide the physician’s treatment decision.