Dr. Bruce Gao
Research Focus
Dr. Bruce Gao
The South Carolina SmartState Endowed Chair in Biofabrication Engineering
Clemson University
Dr. Bruce Gao is a Professor in the Department of Bioengineering at Clemson University. He received his Ph.D in Cardiovascular Engineering from the University of Miami. Dr. Gao is the Co-Principal Investigator of an NSF EPSCoR Track-1 award titled “RII Track-1: AI-enabled Devices for the Advancement of Personalized and Transformative Healthcare in South Carolina (ADAPT in SC)” The awarded RII Track-1 project includes a collaboration among 11 South Carolina colleges including 3 comprehensive research universities, 4 primarily undergraduate institutions, 3 historically black colleges and universities, and 1 community college. The goal of the ADAPT project is to harness the power of artificial intelligence (AI) to improve public health in South Carolina through establishing a research, education, and technology-transfer integrated program focusing on development of AI-enabled personalized biomedical devices.
During the past decade, in the United States, personalization of medical devices has shown the promise to substantially improve health outcomes compared with medical devices developed using current approaches. Simultaneously. AI has been demonstrated the potential of enabling machines to include functionalities that mimic the human mind’s perception, learning, problem-solving and decision-making capability, uniquely replicating and revealing personalized health information from data obtained from individuals as well as groups of patients with similar health conditions. Advances in AI-enabled biomedical devices have reached unprecedented performance levels that have superset those of conventional devices and achieved proficiency in tasks previously thought impossible. ADAPT in SC will leverage the recent surge in biomedical device development in SC to build the state’s capacity for developing AI enabled Biomedical devices. By establishing the state as a national exemplar in development of smart biomedical devices, ADAPT will support the priorities outlined in South Carolina Vision 2030.
Organ Printing Program
Dr. Gao has been heavily involved in the previous two SC’s NSF EPSCoR RII Track-1 programs: in the Organ Printing program, Dr. Gao used a laser beam to pattern individual cells to study various cell-environment interactions for manufacturing better tissue engineered medical devices. The influence of environment on a cell’s function in a tissue depends on many factors, such as the interactions of a cell with neighboring cells, the extracellular matrix (i.e., immediate environment), and physical forces, just to mention a few. Throughout the research, Dr. Gao’s research team is among a few international research groups that can manipulate live cells in a tissue culture dish with a sub micron spatial accuracy. Using the laser patterning system combined with the micro fabrication technique developed in Dr. Gao’s lab, they studied stem cell and cardiomyocyte interaction in a biochip-based model. Their findings suggest that nanotube-mediated stem cell to cardiac muscle cell transfer of mitochondria might contribute to the functional improvement observed in clinical studies on stem cell-based therapy, although in many cases the transplanted stem cells could not be found after the functional improvement had been observed.
MADE in SC
In the recently finished NSF RII program (MADE), Dr. Gao collaborated with his colleague professor, Dr. Tong Ye (Associate Professor Clemson Bioengineering located at MUSC), and developed an AI-assistant that is a non-destructive, and dye-free optical probe for in situ cell viability detection. Their results demonstrated that deep learning significantly improved the outcome of the cell image segmentation and classification. With appropriate training, the deep learning method can achieve 90% accuracy in chondrocyte viability measurement. Currently, they are developing this probe-based biomedical device to be used in clinical for tissue repair. The corresponding AI techniques are used in Dr. Gao’s NIH R01 and DARPA research project.