Johns Hopkins APL Studying Cancer

Throughout his nearly three decades at the Johns Hopkins Applied Physics Laboratory (APL), Dave Porter, a modeling and estimation engineer at APL, has contributed research in his work ranging from ballistic missile accuracy to intelligence surveillance, and groundwater remediation but today are also studying issues related to screening for breast cancer.

Researchers are aware that although preventative screening has reduced breast cancer mortality rates, the systems that are used for detection are not perfect. Present day mammography screening is expensive and requires significant medical infrastructure.

Ultrasounds are often recommended for women with dense breast tissue but sometimes produce false positives and prompt unnecessary biopsies. Magnetic Resonance Imaging (MRI) is also used to detect breast cancer, but it can be available on a limited expensive basis.

Porter and a team of co-inventors at APL had previously developed and patented a technology called "Upstream Data Fusion" (UDF) that leverages AI to combine multiple sources of data able to convey better information than any one source could provide.

According to Porter, the first level of UDF technology is for finding objects. In a medical setting, it applies to the kinds of diseases that are characterized by anomalous tissue or nodes. Porter and his team realized UDF technology could be used to detect lung diseases or other cancers.

Porter and his team understood that there was a gap to fill on the front lines of breast cancer detection. The team then began working with others in the field to study how data fusion and machine learning could be applied to optimize breast cancer detection efforts. The team found than when image registration is combined with UDF technology, the location of a potential breast lesion may be located more quickly and with more accuracy.

Porter and his team were happy to see that APL's Tech Transfer had signed a commercial option with "Curing Women's Cancer", an Arizona-based Foundation, to do a feasibility study incorporating APL's patented data fusion and image registration technologies into an AI-assisted low cost, portable ultrasound screening solution to use to detect breast cancer.

The goal for researchers is to lower costs, improve outcomes, and broaden access to breast cancer screening in underserved parts of the U.S. and the world. At this point, APL researchers report that they are hopeful to continue this research and ultimately submit their technologies in this field to FDA for clearance.