“Microfluidics pressure sensors for the remote monitoring of eye and brain diseases”

Speaker: Murat Baday, Ph.D., Co-Founder, Smartlens

Abstract: Reducing intraocular pressure (IOP) is the only effective treatment for glaucoma, a disease that may lead to irreversible blindness due to optic nerve damage. Despite using pharmaceuticals and surgery to lower IOP, patients still progress from normal vision to blindness with current event-based management. The reasons that glaucoma-related blindness remains a major public health challenge are complex and involve health care disparities, and an unmet need to provide the clinician with a better profile of the IOP variation in an individual patient. This profile of IOP variation is a phenotype, and there is no clinical factor that predicts IOP variation. Currently, office-based IOP data does not provide sufficient IOP data throughout the day to characterize a patient’s IOP phenotype of variation. To meet this need, Smartlens, Inc. has developed a microfluidic strain sensor embedded in a comfortable and affordable soft contact lens (miLens), that provides an electronic-free, wearable, on-demand, smartphone-enabled readout, which captures variations in IOP. The miLens converts small strain changes to a large fluidic volume expansion detectable by a smartphone camera and it is a wire-free, more comfortable solution to the currently marketed telemetry devices, which are made with metallic sensors and antennae. Furthermore, our team is implementing the technology to build minimally invasive sensors to measure intracranial pressure (ICP) in the brain that could be used to monitor several brain diseases including hydrocephalus, traumatic brain injuries, and brain tumors.

“Smart Toilet: A window to precision health”

Speaker: Seung-min Park, Ph.D.,  Instructor, Urology Department, Stanford University School of Medicine

Abstract: Technologies for the longitudinal monitoring of a person’s health are poorly integrated with clinical workflows, and have rarely produced actionable biometric data for healthcare providers. Here, we describe easily deployable hardware and software for the long-term analysis of a user’s excreta through data collection and models of human health. The ‘smart’ toilet, which is self-contained and operates autonomously by leveraging pressure and motion sensors, analyses the user’s urine using a standard-of-care colorimetric assay that traces red–green–blue values from images of urinalysis strips, calculates the flow rate and volume of urine using computer vision as a uroflowmeter, and classifies stool according to the Bristol stool form scale using deep learning, with performance that is comparable to the performance of trained medical personnel. Each user of the toilet is identified through their fingerprint and the distinctive features of their anoderm, and the data are securely stored and analyzed in an encrypted cloud server. The toilet may find uses in the screening, diagnosis and longitudinal monitoring of specific patient populations. 

This is a joint seminar host by PHIND & eWEAR