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GTC 2018 Silicon Valley

S8471 - From Bits to Bedside: Translating Large-Scale Routine Clinical Datasets into Precision Mammography

Session Speakers
Session Description

We'll demonstrate how to use deep learning (DL) approaches to translate big data from routine clinical care into medical innovation that directly improves routine clinical care. Typically, large healthcare institutions have sufficient quantities of clinical data to facilitate precision medicine through a DL paradigm. However, this clinical data is hardly translated into direct clinical innovation because computer algorithms cannot readily ingest or reason over it. Using routine mammographic screening data for breast cancer as an example, we first downloaded over 30,000 free text pathology reports and used long short term memory DL algorithms to infer cancer outcomes for individual patients. We then labeled over 700,000 mammographic views of breast imaging with our inferred pathology outcomes. Finally, we trained convolutional neural network DL algorithms to directly predict pathology outcomes from breast imaging. With our approach, we demonstrate how to leverage DL to realize precision oncology and significantly improve the interpretation of routine screening mammography for millions of women using routine clinical big data.


Additional Information
Pathology, Medical Imaging and Radiology
Healthcare & Life Sciences
All technical
Talk
25 minutes
Session Schedule