Unmet Need
· Retained foreign bodies (RFBs) are remnant materials following neurosurgery, often imperceptible cotton tufts, that can lead to neurological impairment and textilomas (Bechtold et al 2020). The incidence is estimated to be as high as 1 in 100 operations, and cost hospitals millions in healthcare costs and litigation (Whang et al. 2009). Existent methods of RFB location, such as radiofrequency-tagged surgical sponges, lack efficient regiospecificity for location in the brain parenchyma, and have limited use clinically. Therefore, there is a strong need for automated methods to pinpoint RFBs for a successful recovery after neurosurgery.
Technology Description
· Researchers at Johns Hopkins have developed a technology to aid neurosurgeons in the fast and regional detection of inconspicuous RFBs, to aid in removal and prevent negative health outcomes. The technology uses deep learning methods and ultrasound images to detect and define the boundaries of the foreign object, eliminating human judgment and minimizing additional tissue damage in locating and removing RFBs in the future.
Value Proposition
· Near automatic, sub-second analysis on an single-click graphical interface
· > 99% detection accuracy at micrometer to millimeter scale
· Advanced, automatic edge detection with high precision to pinpoint location of any RFBs
Stage of Development
· Ex vivo data has been collected on porcine brains demonstrating excellent accuracy
· Human surgery applications have also been assessed, with a future focus on software integration with mobile point-of-care platforms, improvements to multi-point detection, and diversification of RFB findings
Data Availability
· Data available upon request, and in the publications below.
Publications
1. Mahapatra, S. et al., Medical Imaging 2021: Ultrasonic Imaging and Tomography (2021).
2. Bechtold, R. et al., 2020 Design of Medical Devices Conference (2020).
3. Abramson, H. et al., Frontiers in Surgery, (2022).
