Stanford researchers at the Snyder Lab have developed a novel software application, called the Metabolic Subphenotype Predictor, which predicts if a patient is insulin resistant through continuous glucose monitoring.
Stanford researchers have designed a remote digital health platform to assist diagnosis and management of some inflammatory skin conditions, such as eczema.
Stanford researchers at the Lee Lab have developed a new system and method for measuring pathology then applying a novel algorithm to optimize neurostimulation therapy for altering pathology for treatment of neurodegenerative diseases.
Stanford researchers at the Lee Lab have developed a method to understand whole-brain circuit mechanisms underlying neurological disease and its application to predict the outcome of therapeutic interventions.
Stanford researchers have developed novel designs for 3D-printed microarray patches (MAPs) that can improve intradermal drug delivery and sampling. These designs support the use of microneedles for minimally invasive therapy administration and diagnostics.
Stanford researchers have developed a patient classification method (healthy, idiopathic, diabetic, etc.) based on a quantitative assessment score derived from autonomic and gastric electrocardiogram (ECG) and electrogastrogram (EGG) data.
A Stanford researcher has designed an artificial lens capsule as a drug delivery device inserted at the time of cataract surgery, to address issues of extended drug delivery to the eye with minimal complications.
Active manipulation of light beams is required for a range of emerging optical technologies, including sensing, optical computing, virtual/augmented reality, dynamic holography, and computational imaging.
Wound healing is a huge clinical problem. Problematic outcomes of skin wounds can range from under-healing (e.g., chronic/non-healing wounds) to over-healing (e.g., scarring).
Researchers in Prof. Karl Deisseroth's laboratory have patented a revolutionary technique that can be utilized to map neural circuits in the whole brain.
Actigraphy, or the non-invasive study of human activity-rest cycles, is a field of study of growing importance as ambulatory and at-home monitoring of patients becomes more popular.
Researchers at Stanford University have established a deep learning segmentation algorithm for non-contrast CT images to aid clinicians in decision making and improve the speed of symptom to treatment in acute ischemic stroke
Stanford scientists developed a novel strategy that uses resting-state functional connectivity magnetic resonance imaging (rs-fMRI) to determine whether a person will respond to treatment for depression.