The gold standard of treatment for patients with sleep apnea are Positive-airway-pressure (PAP) machines. PAPs provide a one-size-fits-all solution of providing the same therapy in terms of airflow to every patient and every breath. This causes frustration and discomfort for patients, therefore patients don’t purchase PAPs or purchase and don’t use them; leading to 4 times higher chances of stroke and 3 times higher chances of heart attacks as well as huge costs on the healthcare system.

The aim of this proposal is to assist in the development and validation of a dedicated 4D flow MRI analysis software for the evaluation of aortic valve insufficiency. Before being commercialized this software requires validation considering the large amount of data required to be pre-processed (over 2,000 files per case), elemental data corrections, data analysis preparation, data analysis algorithms, and 3D visualization.

Diabetic patients suffer from reduced sensation in their foot which will be leading to foot ulcers that are hard to treat, and often resulting in limb amputation. The existing health-care techniques usually fail to prevent foot ulcers because they cannot monitor the foot in real-time. In this work, we seek to develop smart socks based on textile technology to help patients to artificially feel sensory stimuli that they cannot realistically feel.

Children presenting a fever without known sources are usually checked for bacterial urinary tract infection (UTI) which, in its more complicated forms, can result in permanent kidney damage. The gold standard conventional tests to diagnose complicated urinary tract infections are urine culture and kidney imaging with a radioactive tracer. However, these tests are lengthy (urine culture can take up to 48 hours to provide results) and expensive.

Simulation is being used increasingly to improve medical education by providing students and trainees with greater access and opportunity to learn critical skills without affecting actual patient care. To this end, OtoSim has developed a multi-user training platform and an otoscope tracking device. The multi-user training platform allows the trainee to self-learn while being electronically connected to a central database for monitoring and advice.

Disposable microfluidic devices, also known as labs-on-a-chip, made out of plastic materials have seen increasing applications in chemical and biomedical analysis. In most applications, microfluidic devices usually incorporate small channels and chambers for micro sized dimensions, using heights between a few hundred to a few micrometers. Currently, manufacturing processes have been established to create these sub-millimeter deep features. However, in other applications, higher (or deeper) features of a few millimeters may be needed.

Epilepsy affects an estimated 50 million people worldwide. These people can experience unexpected seizures that makes it risky for them to engage in everyday activities like driving and walking. A portable wireless neuromonitoring headset prototype that is worn on the head has been developed by Avertus Inc. to address this issue. The headset is designed to read brain waves, and, through a wireless connection to a cell phone, warn the wearer that the device has measured brain activity characteristic with an oncoming seizure.

Perimeter Medical Imaging (PMI) has developed an investigational imaging device to aid in achieving clear margins during surgical oncology procedures. This project will employ PMI’s device to image multiple types of human tissues, which have been previously removed during elective or medical procedures. This study will correlate the images obtained using PMI’s device with the true microscopic structure of the tissue, as confirmed by a pathologist.

The aim of the internship is for the intern to take up the challenge of detecting and removing noise from brain signals that are recorded using electroencephalogram (EEG). The noise that is of interest in the project is mainly caused by the subject chewing and walking. These noises are found to have caused the inability to have a high accuracy in performing seizure detection using EEG. Machine learning-based approaches are to be taken in the attempt to characterize these noises and subsequently eliminate it from the recorded brain signal.

The aim of this project is to research and develop a new DICOM modality for the optical coherence tomography images obtained by Perimeter’s Optical Tissue Imaging (OTISTM) device. Following the creation of a suitable modality, a novel solution for transfer to and integration with the PACS servers utilized by current Perimeter customers will also be developed. The device currently allows for local storage and review of obtained data, with images only ever transferred onto different servers by Perimeter staff.