To accomplish this, the TR&D3 team (Dr. Emre Ertin, Dr. Santosh Kumar, and Dr. Mani Srivastava) utilized innovative radio frequency (RF) technology and cutting-edge deep-learning models. Here’s a glimpse into our recent endeavors and an invitation for collaboration in shaping the future of healthcare diagnostics:

Designing a Scalable RF Array: Our primary focus was on the development of a scalable RF array meticulously crafted to address the challenge of estimating subcutaneous fluid content within layered tissue.  Comprising a transceiver front-end, flexible antenna design, and central processor, our array integrates agile waveforms synthesis, signal sampling, and advanced learning methods to precisely estimate tissue edema levels.  Rigorously validated through Ansys HFSS simulations, this design lays the foundation for groundbreaking advancements in tissue fluid dynamics assessment.

Pioneering Deep-Learning Solutions: In tandem with our RF array development, we’ve harnessed the power of deep-learning models to tackle the inversion problem inherent in imaging subcutaneous tissues.  By leveraging machine learning techniques, we’re poised to unlock new insights and enhance diagnostic accuracy in healthcare.

Prototype Manufacturing and Pilot Research: As we embark on the manufacturing phase of our prototype array, we’re geared up to support a pilot research program focused on lymphedema detection.  This pivotal initiative represents a significant leap forward in our mission to revolutionize healthcare diagnostics, paving the way for enhanced diagnosis and understanding of tissue fluid dynamics.

To learn more about the mDOT Center’s TR&D1 and the various research outcomes, visit TR&D3: Translation.