Collagen-based hydrogels behave similarly to the native tissue microenvironment, thus are widely used as scaffolds for encapsulating cells or molecules like growth factors. Collagen solution is an injectable liquid until it crosslinks at 37 C and physiological pH.
Stanford researchers have developed a technique to interpret contact events between a human and a device equipped with a force sensor. It can detect and classify distinct touch interactions such as tap, touch, grab, and slip.
Tracking in vivo cell distribution, migration, and engraftment using conventional techniques including MRI, PET/CT and conventional optical imaging is often hindered by low resolution, radioactive risks, and limited tissue penetration depth.
Stanford researchers have developed a novel technique to control proton beams for radiation therapy to deliver a very high, full dose across a tumor in less than one second.
Stanford researchers at the Okamura Lab have prototyped a computerized "pillow" that fits in the hand and uses air pressure to measure involuntary grip force (spastic hypertonia).
Stanford researchers at the de la Zerda Lab have developed an innovative alignment methodology using Optical Coherence Tomography (OCT) in conjunction with histopathology to diagnose cancer or determine tumor margins.
Stanford researchers have used deep learning to create a radiotherapy treatment plan verification algorithm. Patient specific dose verification is traditionally done by checking the dose in a patient-mimicking phantom or by using an independent dose calculation algorithm.
Ear infections are a serious condition, especially in children, and represent a $4B market. Otitis media (OM) is when the middle ear becomes inflamed and affects 90% of children worldwide.
Volumetric contrast-enhanced ultrasound is a new approach to collect 3D imaging data of a contrast signal. So far, analysis of 3D contrast ultrasound has relied on averaging a set of voxels embedded in an ROI or a VOI.
Stanford researchers at the Okamura Lab have prototyped a new retraction device that can reverse growth of a soft growing robot without undesired buckling.
The CheXbert labeler accurately detects the presence or absence of 14 common medical conditions in radiology reports, converting unstructured radiology text into a structured format.