Current Events

Date: Tuesday, November 19, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Chuan Huang, Associate Professor, Department of Radiology and Imaging Science, Director of PET-MIR Research, Emory Univesity
Abstract: 
AI in PET/MR is an active field of research. While the current PubMed search identified limited PET/MR AI publications, it is clear that AI has the potential to be applied to the entire life cycle of PET/MR imaging. PET/MR researchers can learn from advancements both in the MR field and PET field, as well as the general computer vision field. In this talk, the speaker will discuss his lab’s experience in AI in PET/MR Imaging and the state of the art.

Date: Tuesday, November 5, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Sundaresh Ram, Assistant Professor, Department of Radiology and Imaging Science, Emory University
Abstract: 
Computational imaging has a rich history of using tools in the areas of signal processing, imaging physics, and machine learning to extract clinically relevant information from data acquired using medical imaging systems in order to support and improve the diagnosis, and therapy planning and follow-up of various diseases. The emergence of artificial intelligence has further expanded the footprint of this field. In this talk, I will present examples of research in our lab that will demonstrate how, by using tools from these areas, we are able to develop innovative solutions for addressing clinically impactful problems.

Date: Tuesday, October 1, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Puneet Sharma, Associate Professor, Pediatrics - Division of Neonatology, Emory University School of Medicine, Children's healthcare of Atlanta
Abstract: 
Artificial intelligence is transforming the landscape of neonatal care, offering powerful tools to analyze vast amounts of healthcare data, identify patterns, and support clinical decision-making. In this presentation, we explore the current and potential future applications of artificial intelligence in neonatal intensive care. We also discuss the potential hurdles to adoption of this technology and what we can do to ensure that it is deployed effective and safely in the care of vulnerable infants.

Date: Tuesday, September 17, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Azra Ismail, Associate Professor, Department of Biomedical Informatics, Emory University
Abstract: 
The prevailing conversation around the future of work often emphasizes digital advancements shaped by a select few, especially in hubs like Silicon Valley. The increasing global inequalities we see today remind us of the need to decentralize technology development and access. So how do we move towards crafting more democratic technological futures? In this talk, I will reflect on our experiences at MakerGhat in creating spaces for underserved youth to exercise their imagination through hands-on making. I invite us to think about how we can build more equitable futures, by addressing who gets to meaningfully participate in creating technology, and the role of education (and educators) in getting us there.

Date: May 21, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Giri Krishnan, Associate Director, Center for Artificial Intelligence in Science and Engineering, Georgia Institute of Technology
Abstract: 
Sleep is observed across species and seems essential for life. Yet, we are still trying to identify its function and how the complementary role of awake and sleep phase impacts biological and cognitive functions. In a series of work, we identify some of the neural mechanism that result in spontaneous activity during different sleep stages and how replay emerges and its impact on learning and memory. Further, we take inspiration from the neural mechanism of sleep to develop an algorithm for deep learning. The sleep-like state implemented for deep neural networks improved continual learning, and generalization under low data and noisy conditions.

Date: April 16, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Yaomin Xu, Assistant Professor, Biostatistics and Biomedical Informatics, Vanderbilt University Medical Center
Abstract: 
Multimorbidity, where multiple health conditions co-exist non-randomly within an individual, is a growing challenge for healthcare and society. Understanding multimorbidity patterns can lead to better prevention, treatments, and personalized care. The advent of electronic health record (EHR) systems provides a vast trove of data for studying real-world patient health dynamics. However, concerns about the primary design of EHRs for billing and administration raise questions about the consistency and reproducibility of EHR-based research. In this study, we used the International Classification of Diseases (ICD) codes to analyze disease comorbidity patterns and employed network modeling to examine multimorbidity across two major EHR systems. Our findings revealed highly correlated multimorbidity patterns across HER systems, with graph-theoretic analysis confirming the consistency of the multimorbidity networks at local (nodes and edges), global (network statistics) and meso (neighboring connection structures) scales. This result offered new insights for developing an efficient framework to analyze and compare complex structures within the multimorbidity network. Our case study demonstrated that identifying subgraphs within multimorbidity networks is an effective method for detecting disease condition clusters, and, supported by graph spectral characteristics of the multimorbidity networks, we developed a complete online network clustering algorithm as an efficient approach to identify those clusters. To facilitate access to these complex datasets and promote further discovery research and hypothesis generation, we have developed a suite of interactive visualization tools for complex online data analysis leveraging data from multiple EHR/Biobank data sources. These tools are open source, available to the public, and are designed to enable researchers to intuitively explore the complex disease relationships within the multimorbidity networks, thereby enhancing our collective understanding and fostering the development of novel precision medicine solutions in the context of multimorbidities.

Date: April 2, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Varun Mishra, Assistant Professor, Khoury College of Computer Sciences, Northeastern University
Abstract: 
The pervasiveness of sensor-rich mobile, wearable, and IoT devices has enabled researchers to passively sense various user traits and characteristics, which in turn have the potential to detect and predict different mental- and behavioral-health outcomes. Upon detecting or anticipating a negative outcome, the same devices can be used to deliver in-the-moment interventions and support to help users. One important factor that determines the effectiveness of digital health interventions is delivering them at the right time: (1) when a person needs support, i.e., at or before the onset of a negative outcome, or a psychological or contextual state that might lead to that outcome (state-of-vulnerability); and (2) when a person is able and willing to receive, process, and use the support provided (state-of-receptivity). In this talk, I will present my research about when to deliver interventions by exploring and detecting both vulnerability and receptivity. I will start by discussing my work that advances the current state-of-the-art by developing reproducible methods to accurately sense and detect various mental and behavioral-health outcomes like stress and opioid use disorder. Next, I will discuss my work regarding methods to explore and detect receptivity to interventions aimed at improving physical activity and how it can guide the design, implementation, and delivery of future mHealth interventions. Finally, I will discuss some of the current projects my lab is working on to build complete solutions that span the entire life-cycle of a digital health intervention (from sensing to intervention delivery) for various mental and behavioral health outcomes by answering "what,'' "when,'' and "how'' to deliver interventions.

Date: March 5, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Arian Ranjbar, Postdoctoral Fellow
Abstract: 
The last decade witnessed a rapid development of data driven methodologies. As the technology matures, there is a growing desire for its implementation into the healthcare sector, to meet the future demands of an aging population. Norway benefits from a public healthcare system where vast quantities of relatively standardized data are already available. However, the healthcare sector in general suffers from a large technical debt, while adhering to strict modes of operation. In this presentation I will talk about the current status and challenges of AI research and implementation at the largest hospital in the country – Akershus University Hospital. This includes data quality improvement, infrastructure and navigating in a system of IT silos, AI and healthcare ethics, and regulation such as the EU AI act; as well as our first research projects developing models on production data.

Date: Feb. 20, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Mously Dior Diaw, PhD Student
Abstract: 
Machine learning (ML) for healthcare holds many promises such as expediting repetitive tasks thus far sustained by human expertise and facilitating the discovery of novel biomarkers. In this talk, I will explore these two paradigms in the context of electrocardiography. First, I will present our human-in-the-loop framework that allows us to measure QT intervals, during drug safety trials, at low labeling cost. The framework consists of 3 key components: (1) deep learning (DL) based QT measurement with uncertainty quantification (2) expert review of a few DL-based measurements, mostly those with high model uncertainty and (3) recalibration of the unreviewed measurements based on the expert-validated data. Second, I will present our AFICIONADO project, which aims to leverage the accessibility of the ECG test to pre-screen high-risk patients for left ventricular dysfunction (LVD), which is traditionally detected with echocardiography. Such a pre- screening strategy would allow to only refer, for echo, patients displaying an abnormal profile of LVD-related echo parameters as estimated with a ML model built on paired ECG- echo data.

Date: Feb. 6, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Xuhai "Orson" Xu, Postdoctoral Associate
Abstract: 
As the intelligence of everyday smart devices continues to evolve, they can already capture basic health behaviors such as physical activities and heart rates. The vision of a complete human-AI pipeline for health -- from behavior modeling to intelligent intervention -- seems to be within easy reach. Why are we not there yet? Existing computational techniques are still far from being deployable, especially for longitudinal health behavior. In this talk, I will introduce our efforts on datasets, algorithms, and a benchmark platform, towards more robust and generalizable behavior modeling using everyday data. Based on these models, I will introduce interventions driven by both behavior science theory and AI that influence users' behavior and promote their health and well-being.

Date: Jan. 30, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Manasa Kalanadhabhatta, PhD candidate
Abstract: 
Nearly one in six U.S children aged 2-8 years has a diagnosed mental, emotional, or behavioral disorder, with actual rates of prevalence likely being even higher. Obtaining an accurate diagnosis is essential for facilitating treatment and interventions, but is often challenging due to a range of structural and phenomenological issues. Mobile and wearable devices offer an opportunity to fill this gap by enabling convenient, at-home behavioral screening. However, most screening technologies for young children rely on parent reports or behavioral observations, thus ignoring the neural and physiological underpinnings of behavioral disorders. In this talk, I will present my work on building neurophysiologically-grounded mental health screening tools for preschool aged children. First, I will discuss my research toward developing scalable, at-home assessment tools that use mobile and wearable devices to derive new insights of clinical value. Next, I will describe how neurophysiological measures including brain activity, cardiorespiratory signals, and electrodermal activity can be integrated with behavioral data to improve the reliability of assessment tools. I will conclude by outlining opportunities for future research in behavioral screening tools, including personalization and repeatability of assessments as well as the integration of neurophysiologically informed tools into clinical practice.

Date: Jan. 16, 2024 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Wei Jin, PhD
Abstract: 
Many learning tasks in Artificial Intelligence require dealing with graph data, ranging from biology and chemistry to finance and education. Graph neural networks (GNNs), as Deep Learning models have shown exceptional capabilities in learning from graph data. Despite their successes, GNNs often grapple with challenges stemming from data quality and size. This talk emphasizes a data-centric approach to enhance GNN performance. First, I will introduce a model-agnostic framework that enhances the quality of imperfect input graphs, thereby boosting prediction performance. Next, I will demonstrate methods to significantly reduce graph dataset sizes while retaining essential information fro model training. These data-centric strategies not only enhance data quality and efficiency but also complement existing models. Finally, I will introduce recent advances in graph generation and data-efficient learning. Join us to explore innovative approaches for overcoming data-related challenges in graph data mining.