Hepatic, or liver, disease affects more than 100 million people in the U.S. About 4.5 million adults (1.8%) have been diagnosed with liver disease, but it is estimated that between 80 and 100 million adults in the U.S. have undiagnosed fatty liver disease in varying stages. Over time, undiagnosed and untreated hepatic diseases can lead to cirrhosis, a severe scarring of the liver that cannot be reversed. 

Most hepatic diseases are chronic conditions that will be present over the life of the patient, but early detection improves overall health and the ability to manage specific conditions over time. Additionally, assessing patients over time allows for effective treatments to be adjusted as necessary. The standard protocol for diagnosis, as well as follow-up tissue assessment, is a biopsy after the return of an abnormal blood test, but biopsies are time-consuming and pose risks for the patient. Several non-invasive imaging techniques have been developed to assess the stiffness of liver tissue, an indication of scarring, including magnetic resonance elastography (MRE).

MRE combines elements of ultrasound and MRI imaging to create a visual map showing gradients of stiffness throughout the liver and is increasingly used to diagnose hepatic issues. MRE exams, however, can fail for many reasons, including patient motion, patient physiology, imaging issues, and mechanical issues such as improper wave generation or propagation in the liver. Determining the success of MRE exams depends on visual inspection of technologists and radiologists. With increasing work demands and workforce shortages, providing an accurate, automated way to classify image quality will create a streamlined approach and reduce the need for repeat scans. 

Professor Jun Ueda in the George W. Woodruff School of Mechanical Engineering and robotics Ph.D. student Heriberto Nieves, working with a team from the Icahn School of Medicine at Mount Sinai, have successfully applied deep learning techniques for accurate, automated quality control image assessment. The research, “Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results,” was published in the Journal of Magnetic Resonance Imaging.

Using five deep learning training models, an accuracy of 92% was achieved by the best-performing ensemble on retrospective MRE images of patients with varied liver stiffnesses. The team also achieved a return of the analyzed data within seconds. The rapidity of image quality return allows the technician to focus on adjusting hardware or patient orientation for re-scan in a single session, rather than requiring patients to return for costly and timely re-scans due to low-quality initial images.

This new research is a step toward streamlining the review pipeline for MRE using deep learning techniques, which have remained unexplored compared to other medical imaging modalities.  The research also provides a helpful baseline for future avenues of inquiry, such as assessing the health of the spleen or kidneys. It may also be applied to automation for image quality control for monitoring non-hepatic conditions, such as breast cancer or muscular dystrophy, in which tissue stiffness is an indicator of initial health and disease progression. Ueda, Nieves, and their team hope to test these models on Siemens Healthineers magnetic resonance scanners within the next year.

Publication
Nieves-Vazquez, H.A., Ozkaya, E., Meinhold, W., Geahchan, A., Bane, O., Ueda, J. and Taouli, B. (2024), Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results. J Magn Reson Imaging. https://doi.org/10.1002/jmri.29490

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Chaouki Abdallah, Georgia Tech's executive vice president for Research (EVPR), has been named the new president of the Lebanese American University in Beirut.  

Abdallah, MSECE 1982, Ph.D. ECE 1988, has served as EVPR since 2018; in this role, he led extraordinary growth in Georgia Tech's research enterprise. Through the work of the Georgia Tech Research Institute, 10 interdisciplinary research institutes (IRIs), and a broad portfolio of faculty research, Georgia Tech now stands at No. 17 in the nation in research expenditures — and No. 1 among institutions without a medical school.  

Additionally, Abdallah has also overseen Tech's economic development activities through the Enterprise Innovation Institute and such groundbreaking entrepreneurship programs as CREATE-X, VentureLab, and the Advanced Technology Development Center. 

Under Abdallah's strategic, thoughtful leadership, Georgia Tech strengthened its research partnerships with historically Black colleges and universities, launched the New York Climate Exchange with a focus on accelerating climate change solutions, established an AI Hub to boost research and commercialization in artificial intelligence, advanced biomedical research (including three research awards from ARPA-H), and elevated the Institute's annual impact on Georgia's economy to a record $4.5 billion.  

Prior to Georgia Tech, Abdallah served as the 22nd president of the University of New Mexico (UNM), where he also had been provost, executive vice president of academic affairs, and chair of the electrical and computer engineering department. At UNM, he oversaw long-range academic planning, student success initiatives, and improvements in retention and graduation rates. 

A national search will be conducted for Abdallah's replacement. In the coming weeks, President Ángel Cabrera will name an interim EVPR. 

Robots that can run, jump, and even talk have shifted from the stuff of science fiction to reality in the past few decades. Yet even in robots specialized for specific movements like running, animals are still able to outmaneuver the most advanced robotic developments. 

Georgia Tech’s Simon Sponberg recently collaborated with researchers at the University of Washington, Simon Fraser University, University of Colorado Boulder, and Stanford Research Institute to answer one deceptively complex question: Why can’t robots outrun animals? 

“This work is about trying to understand how, despite have some really amazing robots, there still seems to be a gulf between the capabilities of animal movement and what we can engineer,” says Sponberg, who is Dunn Family Associate Professor in the School of Physics and School of Biological Sciences. 

Recently published in Science Robotics, their study systematically examines a suite of biological and robotic runners to figure out how to further advance our best robotic designs. 

“In robotics design we are often very component focused — we are used to having to establish specifications for the parts that we need and then finding the best component solution,” said Sponberg, who also serves on the executive committee for Georgia Tech's Neuro Next Initiative. “This is of course not how evolution works. We wondered if we systematically analyzed the performance of animals in the same component way that we design robots, if we might see an obvious gap.” 

The gap turns out not to be in the function of individual robotic components, but rather the ability of those components to work together in the seamless way biological components do, highlighting a field of opportunity for new research in robotic development. 

“This means that the frontier is not necessarily figuring out how to design better motors or sensors or controllers,” says Sponberg, “but rather how to integrate them together — this is where biology really excels.” 

Read more about man versus machine and the future of bioinspired robotics here.

Jaydev Desai has been named a recipient of the 2024 IEEE RAS George Saridis Leadership Award in Robotics and Automation from the IEEE Robotics and Automation Society (RAS). Dr. Desai will receive this accolade at the 2024 IEEE International Conference on Robotics and Automation (ICRA2024) to be held in Yokohama Japan.

Named in honor of Professor George Saridis, the award recognizes outstanding contributions of an individual for their exceptional leadership, and dedication that benefit the IEEE Robotics and Automation Society. Desai was nominated by Torsten Kroeger, Chief Science Officer at Intrinsic, who stated, “Jaydev has made pioneering contributions in Medical Robotics and Swarm Robotics in addition to significant leadership and service activities within the IEEE Robotics and Automation Society (RAS)."

Jaydev P. Desai is currently a Professor at Georgia Tech in the Wallace H. Coulter Department of Biomedical Engineering and holds the G.P. “Bud” Peterson and Valerie H. Peterson Faculty Professorship in Pediatric Research. He is the Associate Chair for Undergraduate studies in BME at GT, founding Director of the Georgia Center for Medical Robotics (GCMR), and an Associate Director of the Institute for Robotics and Intelligent Machines (IRIM). He completed his undergraduate studies from the Indian Institute of Technology, Bombay, India, in 1993. He received his MA in Mathematics in 1997 and MSE and Ph.D. in Mechanical Engineering and Applied Mechanics in 1995 and 1998 respectively, all from the University of Pennsylvania. He was also a Post-Doctoral Fellow in the Division of Engineering and Applied Sciences at Harvard University.

He is a recipient of several NIH R01 grants, NSF CAREER award, and was the lead inventor on the “Outstanding Invention in the Physical Science Category” at the University of Maryland, College Park, where he was formerly employed. He is also the recipient of the Ralph R. Teetor Educational Award and the 2021 IEEE Robotics and Automation Society (RAS) Distinguished Service Award. He has been an invited speaker at the National Academy of Sciences “Distinctive Voices” seminar series and also invited to attend the National Academy of Engineering’s U.S. Frontiers of Engineering Symposium. He has over 200 publications, is the founding Editor-in-Chief of the Journal of Medical Robotics Research, and Editor-in-Chief of the four-volume Encyclopedia of Medical Robotics. At 2018 ICRA, his prior work was the finalist for “IEEE RAS Award for the Most Influential Paper from ICRA 1998” (20-years impact). His research group has received several accolades including the best student paper award, best symposium paper award, cover image of IEEE Transactions on Biomedical Engineering, and featured article in the IEEE Transactions on Biomedical Engineering. His current research interests are primarily in the areas of image-guided surgical robotics, pediatric robotics, endovascular robotics, and rehabilitation and assistive robotics. He is a Fellow of IEEE, ASME, and AIMBE.

- Christa M. Ernst

A new research center in the Institute for Electronics and Nanotechnology (IEN) will help bring together human-centered bioelectronics technology research to improve human healthcare and expand human-machine interface technologies.

The Wearable Intelligent Systems and Healthcare (WISH) Center will work to push innovation in wearable sensors and electronics technologies. Focus areas of the center will include electronics, artificial intelligence, biological science, material sciences, manufacturing, system design, and medical engineering.

“We are excited by the promise of bioelectronics improving human health and all the exciting science engineering that is required to make it a reality,” said Michael Filler, interim executive director of IEN.

WISH is directed by W. Hong Yeo, associate professor in Georgia Tech’s George W. Woodruff School of Mechanical Engineering and the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory, and Yuhang Hu, associate professor in the School of Chemical and Biomolecular Engineering at Georgia Tech.

“I founded WISH to bring together Georgia Tech’s expertise in various disciplines and to create opportunities for developing wearable bioelectronics and human-machine technologies leading to better lives and communities,” said Yeo.

Yeo’s research focuses on developing soft sensors, electronics and robotics for health monitoring and disease diagnosis at the intersection of human and machine interaction. Other researchers in the center represent disciplines from across Georgia Tech’s Colleges of Engineering, Computing, Sciences, Design, and Liberal Arts; Emory University; and Children’s Healthcare of Atlanta.

WISH will be one of IEN’s 10 strategic research centers, along with the 3D Systems Packaging Research Center, a graduated NSF Engineering Research Center focusing on advanced packaging using 2.5D and 3D heterogeneous integration technologies, and the Georgia Electronic Design Center, one of the world’s largest university-based semiconductor research centers. WISH is an evolution of the Center for Human-Centric Interfaces and Engineering, which received seed funding from IEN to focus on collaborative research for human-centered design, biofeedback control, and integrated nanosystems to advance human-machine interaction in the scope of healthcare.

IEN supports early-stage research in underfunded research areas that span all disciplines in science and engineering through its seed grant programs, which focus on research in biomedicine, electronics, optoelectronics and photonics, and energy applications.