Keynote speakers BHI-BSN-2022


Prof. Jeffrey Palmer, Massachusetts Institute of Technology Lincoln Laboratory 

AI-enabled Sensing and Interventions for Global Health.

ABSTRACT: The challenges and opportunities to improve the global health cycle are at critical inflection points under the strain of a world-wide pandemic, international conflict, and large-scale environmental disasters. AI-enabled sensing, decision support, and actions can leverage the enormous data generated and consumed through the global health steps of monitoring, diagnosis, intervention, training, prevention, and informing the public. This presentation will discuss how body sensor networks and health informatics platforms can work in concert with population-level and environmental sensing to assess health threat phenomenology, exposure dosimetry, medical intervention efficacy. These advances can be used to scale interventions, guide health and emergency response policy, enhance training of healthcare providers and first responders, and more effectively engage the public.

Prof. Roisin M. Owens , Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom

Bioelectronic tools to study the gut-brain axis.

ABSTRACT: Polymeric electroactive materials and devices can bridge the gap between hard inflexible materials used for physical transducers and soft, compliant biological tissues. An additional advantage of these electronic materials is their flexibility for processing and fabrication in a wide range of formats. In this presentation, I will discuss our recent progress generating 3D conducting polymer devices, to simultaneously host and monitor complex multi-cellular models of tissues and organs. Electrophysiological recording of parameters such as tissue impedance, epithelial and endothelial barrier tissue integrity and neuronal activity, are all made possible thanks to the conducting polymer devices and are validated with traditional biological readouts such as immunofluorescence or cytokine analysis. Building on our previous work that showcased a bioelectronic model of the human intestine, we are now incorporating elements of the microbiome and the immune system as well as the enteric nervous system. Coupling this model with our model of the neuro-vascular unit (including blood brain barrier) currently in progress, will bring us to our goal of a physiologically representative in vitro model of the gut-brain-microbiome axis. Alongside our in vitro work, I will show how our recent work on developing electronic probes to study the enteric nervous system. Transitioning from in vitro human and rat to in vivo rat models allows us to integrate electrophysiological recordings of neuronal activity with tissue impedance to really begin to unravel gut-brain axis signaling.

Dinggang Shen, Adjunct professor, Professor and Dean, School of BME, Shanghaitech University

Deep Learning based Medical Image Reconstruction

ABSTRACT: This talk will introduce various deep learning methods we developed for fast MR acquisition, low-dose CT reconstruction, and low-cost and low-dose PET acquisition. The implementation of these techniques in scanners for real clinical applications will be demonstrated. Also, comparisons with state-of-the-art acquisition methods will be discussed.

Riccardo Bellazzi, Professor of Bioengineering and Biomedical Informatics, University of Pavia

Building trustworthy AI systems with reliable components
ABSTRACT: AI medical systems, designed to support diagnosis, therapy planning and monitoring, have a long history, but recently they received a renewed strong attention due to the advancements in machine and deep learning and to the large and increasing availability of digital data. The need of protecting citizens, providing them with safeguards against misuse of AI approaches, and in particular of data-driven technologies, has pushed towards the implementation of “trustworthy” AI systems, lawful, ethical and robust. This talk will discuss how components, based on reliability principles, may provide the basis for the design and implementation of successful AI solutions. Finally, the talk will advocate that only a proper socio-technical approach will eventually provide trustworthy systems.

Event Search