by | Feb 27, 2020 | Green, Olga, Mutic, Sasa, Park, Chunjoo (Justin), Zhang, Hao
— Technology Description
A team of researchers at Washington University School of Medicine developed deep learning image processing techniques to improve MRI diagnostics and potentially enable faster, more precise MRI-guided radiation therapy without exposing patients to radiation from CT imaging. S…
by | Jan 6, 2020 | Cahill, Alison, Cuculich, Phillip, Schwartz, Alan, Wang, Yong
— Technology Description
Researchers at Washington University in St. Louis have developed an electromyometrial imaging (EMMI) method to non-invasively monitor uterine contractions. During pregnancy, many women experience preterm contractions. Sometimes these contractions progress to pre-term labor a…
by | Nov 18, 2019 | Altman, Michael, Green, Olga, Kavanaugh, James, Li, Hua, Mutic, Sasa, Wooten, Hasani
— Technology Description
A team of researchers at Washington University has created a machine learning method to quickly and reliably validate patient contours in digital medical images for radiation therapy and computer aided image analysis.
Currently, automated contouring tools for delineating tu…
by | Oct 22, 2019 | Genin, Guy, Leuthardt, Eric, Margolis, Daniel, Somers, Thane, Yarbrough, Chester
— Technology Description:
Drs. Eric Leuthardt and Guy Genin have devised a medical device designed for faster and safer endoscopic surgeries. During these surgeries, the camera lens of the endoscope can become obscured with blood, fog and debris. This slows down the procedure, as the surgeon has to r…
by | Oct 22, 2019 | Dodt, Jeremy, Hughes, Scott, Mantia, Jill, Melnek, Christine, Reynolds, Steven, Whipple, Toni
— Background: Current EHR systems require that scheduling changes are performed by the scheduling team acting as a “hub”. The provider is unable to see availability of rooms and resources, especially of those that open up due to pending cancellations. This results in inefficiencies that co…