We are pushing the envelope of medical sensors and devices to change the ways we study and treat diseases and injuries. Instead of incremental improvements on current technologies, we are focusing on revolutionary approaches that will result in new treatment modalities and groundbreaking medical research.

The Knight Campus and the University of Oregon have multiple research clusters that focus on the development and implementation of implantable and wearable devices, materials, data science, etc. to meet today's and future medical needs.

Major problems we are trying to solve:

• Medical technologies for personalized medicine: With the belief that every person is unique and each patient responds to his/her medical treatments differently, we are developing sensors, electronic devices, materials, and predictive models to tailor the therapy so it is specific to the individual’s conditions for optimal outcomes and safety.
• Innovation in medical research: By incorporating innovations from other fields, we are making devices that promote new medical research and studies. For example, we are currently developing a number of sensor technologies for real-time measurements of physical parameters at bones and muscles to study the treatment effectiveness of various orthopedic injuries and diseases.
• Medical manufacturing technologies: New manufacturing technologies are being developed to make medical treatments safer and more effective. Examples include the use of 3D printing technology to build custom implants for patients, and a new sensor that can monitor the quality of cells during the manufacturing process.

Participating Research Groups

• Theranostic orthopedic devices for accelerating bone and tissue regeneration (Ong, Guldberg, Hettiaratchi).
• The use of sensors and data science to predict the success and patient’s responsiveness to a therapy (Guldberg).
• Medical sensors to guide treatment regimen for orthopedic injuries (Ong, Guldberg, Hahn).
• New manufacturing techniques to produce patient-tailored devices (Gardner, Dalton, Guldberg, Lindberg, Ong).
• Wearable sensors for tracking human physiological conditions (Ong).
• Biosensors and molecular probes (Ong, Johnson, Pluth, Jasti, Haley).
• Knight Campus Graduate Internship Program Molecular Sensor track.

Biological systems are the most complex entities in the known universe. Patterns are discernible in their complex states and in the interactions among their components. Machine learning is a branch of Artificial Intelligence that shows how machines can learn patterns from data with minimal human input. Machine learning has proved to be an invaluable tool in modeling these complex systems, as the patterns may be difficult or impossible for humans to perceive reliably.

Machine learning enables a host of predictive capabilities, which include diagnosis of disease from medical images, prognosis of risk and therapeutic response from patient data, protein function prediction from amino acid sequence, design of novel proteins, and optimization of biomaterials. In these and other domains, machine learning has been harnessed to improve human health and advance sustainability.

Major problems we are trying to solve:

• Automated detection and diagnosis of disease from medical images.
• Automated histology and immunohistochemical image analysis.
• Prediction of health risks and therapeutic response through data science.
• Predictive models of protein sequence-function relationships.
• Design of novel proteins with unique properties for therapeutics.
• Optimization of biomaterials for medical devices and therapeutic delivery.

Participating Research Groups

• Medical image analysis.
• Prediction of therapeutic response (Guldberg).
• Protein engineering (Hettiaratchi, Hosseinzadeh, Plesa).
• Computational methods (Cresko, Hosseinzadeh).
• Optimization of biomaterials (Dalton, Hettiaratchi).
• 3D printing of functional biomaterials (Dalton, Gardner, Guldberg, Ong).