Behind the great success of biomedical imaging, a crisis is looming: the number of imaging studies is growing exponentially; the workload of radiologists is increasing dramatically; the health-care cost related to imaging is rising rapidlyWe are facing a grant new challenge: image data explosion (a manifestation of big data in biomedical imaging): Modern imaging systems generate enormous data, far exceeding human abilities for interpretation, but what is paramount are not the images themselves, rather the clinically relevant information contained within the images; therefore, our long-term goal is to develop and validate comprehensive, high-performance computational tools that automatically and quantitatively extract clinically relevant information from images to support clinical decision making and facilitate precision medicine. To demonstrate the immediate, measurable impact of our research, we have chosen pulmonary embolism as our initial research platform because the Surgeon General has declared pulmonary embolism a major national health problem, causing more deaths than breast cancer, AIDS, and motor vehicle accidents combined; with laudable efforts to diagnose pulmonary embolism, the number of CT studies for suspected pulmonary embolism has been increasing dramatically, while incorrect CT interpretations are frequent in general practice (10-14% over/under-diagnosis); therefore, there is a clinical need to (1) mitigate radiologists workloads and (2) improve the efficiency and accuracy for pulmonary embolism diagnosis using CT. Our objective is to address this clinical need by exploiting radiologist-computer synergy, delivering two important outcomes: (a) a new methodology that transcends the current paradigm from mere detection of emboli to simultaneous patient-level diagnosis, embolus-level detection, and over-diagnosis prevention; and (b) a next-generation, high-performance system that will be able to assist radiologists in quickly excluding negative patients without overlooking positive patients, accurately localizing individual emboli to support personalized treatments through risk stratification, and actively preventing PE over-diagnosis, exerting an important positive impact on clinical practice associated with pulmonary embolisma life-threatening condition.
|Effective start/end date||7/1/16 → 4/30/22|
- HHS: National Institutes of Health (NIH): $2,548,122.00
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