Hydrocephalus is the buildup of cerebrospinal fluid in the ventricular system that must be flushed out, or it will cause serious health related issues and even morbidity. It occurs in 1.5 out of every 1000 births, and is particularly dangerous for young children.1 The current standard of care to flush out this fluid is to implant a shunt, connecting the ventricles to the peritoneal cavity, and depositing the CSF there, where it is reabsorbed. However, the device has a malfunction rate of 12.9% after 30 days, and 28.8% after one year.2 A malfunction in the shunt system can prove to be either deadly or permanently scarring for a patient, and there currently is no method to noninvasively monitor this commonly used device. The potential impact of a malfunction detection system would be to provide access to previously unavailable flow data along the extent of the shunt line. Therefore, the device, if implemented in all hydrocephalus shunt cases as a new standard of care, would be able to provide data for all types of current shunt designs. This would also have a significant clinical impact, in that diagnosis would be streamlined and, after collection of baseline data, be achievable via machine learning algorithms for each patient. By eliminating the need for comprehensive, often time-intensive, expensive tests, patient risk would be minimized, while clinicians could more efficiently identify and rectify a blockage. Therefore, a shunt monitoring device would have a large market and clinical impact.
Malfunction detection in ventriculoperitoneal shunts