PHIND Seminar Series: Moving Magnetic Resonance Imaging Towards a Low-Cost High-Value Medical Imaging Modality
Akshay Chaudhari, Ph.D.
Instructor
Department of Radiology
Stanford University
12:00pm – 1:00pm Seminar & Discussion
RSVP Here: https://www.onlineregistrationcenter.com/AChaudhari
The seminar will be available via a zoom live stream.
Meeting URL: https://stanford.zoom.us/j/257831873
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
Meeting ID: 257 831 873
ABSTRACT
Magnetic Resonance Imaging (MRI) is a medical imaging modality that offers exquisite resolution and soft-tissue contrast. It is an integral component in diagnostic radiology as well as in basic science research studies due its sensitivity in detecting subtle variations in tissue structure. While MRI can provide a rich source of information, typical acquisition times of 30-40 minutes can limit further widespread use, increase costs, and diminish the patient experience. Moreover, the high-resolution and multi-dimensional MRI datasets can also cause a challenge for efficient and accurate image interpretation. In this talk, through specific examples in musculoskeletal MRI, I will cover recent advances in MRI aided by classical engineering techniques as well as deep learning to substantially reduce the duration of MRI exams and for subsequent image analysis. I will describe how these efforts are helping change the paradigm of MRI by reducing costs and increasing efficiency.
ABOUT
Dr. Akshay Chaudhari is an Instructor in the Radiological Sciences Lab (RSL) and Precision Health and Integrated Diagnostics (PHIND) sections in department of Radiology who works at the interface of radiology and artificial intelligence. His research interests include developing efficient and safer medical imaging acquisition techniques, repeatable and accurate image analysis tools, and on multi-modality sensor fusion. He graduated with honors with a B.S. in Bioengineering from the University of California San Diego in 2012 and completed his Ph.D. from Stanford Bioengineering in 2017 focusing on novel MRI methods to perform rapid quantitative musculoskeletal imaging. Dr Chaudhari received the National Science Foundation Graduate Research Fellowship, the Whitaker Fellowship, and the Siebel Fellowship to support his doctoral research. Dr. Chaudhari is the winner of the ISMRM W.S. Moore Young Investigator Award, and has won 6 additional young investigator awards for his work on advanced medical imaging acquisition and analysis techniques, and is a Junior Fellow of the ISMRM.
Hosted by: Sanjiv Sam Gambhir, M.D., Ph.D.
Sponsored by the PHIND Center and the Department of Radiology
PHIND Seminar Series
11:00-11:30 AM | Dr. Anoop Rao, M.D., M.S.
“Wearable Sensing for Neonates”
Clinical Instructor, Pediatrics (Neonatology)
Lucile Packard Children’s Hospital
Stanford University School of Medicine
11:30-12:00 PM | Dr. Eric Dy, Ph.D.
“Crowdsourced data and machine learning to design the future of prenatal care”
Co-founder and CEO
Bloomlife
12:00pm – 1:00pm Seminar & Discussion
RSVP Here: https://www.onlineregistrationcenter.com/PHIND061620
This seminar will be available in person and via a Zoom live stream.
Meeting URL: https://stanford.zoom.us/j/92848236311
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 928 4823 6311
Dr. Anoop Rao Bio
Anoop Rao is a Clinical Instructor in Pediatrics (Division of Neonatology) at the Lucile Packard Children’s Hospital. After completing early medical training in India, he obtained his MS from MIT, and completed clinical training in Pediatrics from Columbia and fellowships in Neonatal Critical Care from Stanford and Biomedical Informatics from Harvard. He spent over 5 years at AgaMatrix, a startup focused on developing high-accuracy blood glucose meters.
At Stanford, his research is on contact and non-contact monitoring of infants. Anoop collaborates extensively with industry and is actively supported by NIH/Maternal and Child Health Research Institute.
Dr. Eric Dy Bio
Eric Dy, PhD is co-founder and CEO of Bloomlife, a women’s health company designing remote prenatal care solutions to improve the health of women and babies. Eric brings unique perspective on the opportunities and challenges in emerging healthcare technologies and delivery models informed by multidisciplinary technical expertise leading business development for Europe’s leading R&D institute, imec. Eric earned his BSc in Bioengineering from Cornell and his MSc and PhD in Biomedical Engineering from UCLA. Bloomlife has been recognized for their pioneering work winning Fast Company World Changing Ideas, Johnson & Johnson Quickfire Challenge, Richard Branson’s Extreme Tech Challenge, MedTech Innovator Award, and speaking at the White House Precision Public Health Summit.
Dr. Eric Dy Abstract
The period from conception through the first 1000 days of life are the most critical for lifelong health and development, yet too often we are failing women and babies at this time. High risk pregnancies are on the rise, access to care is increasingly a problem, and pregnancy complications such as preterm birth now affect 1 in 10 babies. Despite these growing challenges, the way we deliver prenatal care has not fundamentally changed in over 60 years. We need smarter tools, better data, and scalable solutions to improve the health of moms and babies globally.
In this talk Bloomlife co-founder and CEO will share their strategy for designing the future of prenatal care. He will discuss how clinical grade wearables, in the hands of mom, has helped create the largest dataset on pregnancy in the world, and how AI applied to this dataset is seeding breakthrough screening and diagnostic tools to help solve global maternal health issues including preterm birth.
Hosted by: Sanjiv Sam Gambhir, M.D., Ph.D.
Sponsored by: PHIND Center, Department of Radiology and eWEAR Initiative
PHIND Seminar Series: What is in your sweat and what can it mean for health and disease?
11:00 AM – 12:00 PM: Seminar & Discussion
RSVP: https://www.onlineregistrationcenter.com/VShankar
Presenter:
Vishnu Shankar, M.S.
Department of Chemistry
Stanford University
Principal Investigators:
Michael Snyder, Ph.D.
Stanford W. Ascherman, MD, FACS Professor in Genetics
Stanford University
Robert Tibshirani, Ph.D.
Professor of Biomedical Data Science and of Statistics
Stanford University
Richard Zare, Ph.D.
Marguerite Blake Wilbur Professor in Natural Science and Professor, by courtesy, of Physics
Stanford University
Location
Webinar URL: https://stanford.zoom.us/s/99817512229?pwd=QitCTjRXMEdBTWZyd29MTHYyNU5Xdz09
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 998 1751 2229
Password: 489011
ABSTRACT
Sweat is a complex fluid known to be rich in electrolytes, small molecules, and fatty acids. Although adults can sweat up to 10 liters per day, little is still known about the chemical composition of sweat, how this changes, and what are its implications for health and disease. We demonstrate a powerful approach to help elucidate this link, where collecting samples simply requires swabbing a glass slide across one’s forehead in less than 30 seconds. Using the combination of desorption electrospray ionization mass spectrometry and statistical machine learning, our approach can successfully detect over 10,000 metabolites in sweat and identify metabolic changes in the sweat profile related to gender, age, and disease. As an example, we demonstrate in a cohort of 65 subjects the possibility of using just a few metabolites detected in sweat to successfully identify patients with renal disease. More generally, our approach suggests the possibility of using the sweat profile to non-invasively assess individual risk for metabolic diseases in the theme of “Precision Medicine.”
ABOUT VISHNU SHANKAR
Vishnu Shankar recently graduated with his master’s degree in computer science, with a specialization in artificial intelligence from Stanford University. He completed his bachelor’s degree with honors in mathematical and computational sciences in 2018, also at Stanford, with his senior thesis on Bayesian networks for incorporating effect modifiers in meta-analysis. In addition, his background spans biology, mathematics, chemistry, statistics, operations research, physics, and computing. Vishnu has published 6 papers and 3 articles in fields including protein structural prediction, comparison of clinical guidelines cost-effectiveness in type 2 diabetes, development of programs to combat mental illness, cancer diagnosis with analytical chemistry and machine learning, and related areas. He is also the founder of the CARES organization to support peer student wellness at college campuses, for which he won the Asoka Youth Changemaker award sponsored by Boehringer Ingelheim. Vishnu has enthusiastically pursued science since his middle school days and has worked on demonstrating the possibilities of DNA computing, simulating protein folding, studying the genetic modifications in fruits and vegetables, and more. He has been recognized for his scientific research as an Intel Science Talent finalist, Google Science Fair Regional finalist, recipient of the American Institute of Aeronautics and Astronautics Excellence award for his work on epidemiology modeling. Vishnu has interned at Caltech and Genentech, where he applied experimental techniques to purify and study protein behavior including dialysis, titration, chromatography for early stage drug development. He was also selected as one of the two high school students to represent the western US Confucius Institute in Student Leaders Exchange Program in China as a non-native Mandarin speaker.
Hosted by: Sanjiv Sam Gambhir, M.D., Ph.D.
Sponsored by the PHIND Center and the Department of Radiology
Location & Timing
August 5, 2020
8:30am-4:30pm
Livestream: details to come
This event is free and open to all!
Registration and Event details
Overview
Advancements of machine learning and artificial intelligence into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. Sponsored by the Stanford Center for Artificial Intelligence in Medicine and Imaging, the 2020 AIMI Symposium is a virtual conference convening experts from Stanford and beyond to advance the field of AI in medicine and imaging. This conference will cover everything from a survey of the latest machine learning approaches, many use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing building and evaluating machine learning in healthcare applications.
Our goal is to make the best science accessible to a broad audience of academic, clinical, and industry attendees. Through the AIMI Symposium we hope to address gaps and barriers in the field and catalyze more evidence-based solutions to improve health for all.
PHIND Seminar Series: Identifying Fibroblasts Subtypes Contributing to the Progression of Preinvasive to Invasive Lung Adenocarcinoma
Sylvia Plevritis, Ph.D.
Professor of Biomedical Data Science and of Radiology
Integrative Biomedical Imaging Informatics at Stanford
Stanford University
Location
Webinar URL: https://stanford.zoom.us/s/93945120934?pwd=a29GNjFCUzBtWjRsbFdnUnVUOTMzUT09
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 939 4512 0934
Password: 767148
11:00am – 12:00pm Seminar & Discussion
RSVP: https://www.onlineregistrationcenter.com/SPlevritis
ABOUT
Dr. Sylvia K. Plevritis is Professor and Chair of Biomedical Data Science, Professor of Radiology at Stanford University and Program Director of the Stanford Biomedical Informatics Graduate Training Program. Dr. Plevritis leads a computational biology cancer research program that bridges genomics, imaging and population sciences to decipher properties of cancer progression and treatment response. Dr. Plevritis received her Ph.D. in Electrical Engineering and M.S. in Health Services Research, both from Stanford University, with a focus on cancer imaging physics and modeling cancer outcomes, respectively. She has had a primary authorship role on over 100 scientific cancer-related articles. She is a fellow of the American Institute for Medical and Biological Engineering (AIMBE) and Distinguished Investigator in the Academy of Radiology Research. She serves on the NCI Board of Scientific Advisors, the Program Leadership Committee of the Stanford Cancer Institute and the Leadership Council of the Stanford Bio-X Program. Dr. Plevritis has served on numerous NIH study sections, chaired scientific programs for the several professional societies including the American Association for Cancer Research (AACR) and presented keynote lectures across multiple scales of computational cancer biology. Currently, she is the Program Director of the Stanford Center in Cancer Systems Biology (CCSB) and has been a Principal Investigator with the NCI Cancer Intervention Surveillance Network (CISNET) for over fifteen years. She has served as Program Director of the Stanford Cancer Systems Biology Scholars Program (CSBS), and co-Division Chief of Integrative Biomedical Imaging Informatics at Stanford (IBIIS).
Sponsored by the PHIND Center and the Department of Radiology
PHIND Seminar Series: Maintaining Competitive Advantage through Intellectual Capital
Soody Tronson, M.S., J.D.
Founder
Presque
Location & Timing
11:00am – 12:00pm Seminar & Discussion
Webinar URL: https://stanford.zoom.us/s/98817009379?pwd=U21pYnpCTUlxY3U2TEh4RmhvMXpvQT09
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 988 1700 9379
Password: 767148
RSVP: https://www.onlineregistrationcenter.com/STronson
ABSTRACT
Digital health has provided a range of solutions, including health and wellness-related management, machine learning algorithms for pattern recognition, AI for genetic analysis, AI-enhanced clinical decision making, and virtual doctors that use AI for patient intake triage. Intellectual property can play an important role in providing a competitive advantage in the digital health industry. We will explore opportunities and challenges for protecting digital health technologies during the program, including patent eligibility challenges and the use of trade secrets.
ABOUT SOODY TRONSON
Soody Tronson is Founding Managing Counsel at STLG Law Firm, counseling domestic and international clients in IP and technology transactions in a wide range of technologies. In 2016 she formed, Presque, a company developing a line of medical devices for mothers and infants.
Soody has over 25 years of operational experience in technology, business, management, and law in start-up and fortune 100 companies, including Schering Plough Pharmaceuticals, Hewlett-Packard Co., Avantec Vascular, and the law firms of HellerEhrman and Townsend and Townsend.
Soody serves in board and leadership capacities with several organizations including the Association of Women in Science STEM to Market national accelerator; Licensing Executives Society USA/Canada, California Lawyers Association, and the Palo Alto Area Bar Association. Soody is a Commissioner with the city of Menlo Park; and an active hands-on volunteer with several civic organizations including Defy Ventures, an entrepreneurship training program for currently and formerly incarcerated. Soody has instructed courses in IP, licensing, and entrepreneurship at the University of California, Stanford University and European institutions. She is the co-author of the book “Women Securing the Future with TIPPSS for IoT: Trust, Identity, Privacy, Protection, Safety, Security for the Internet of Things.”
Hosted by: Garry Gold, MD
Sponsored by the PHIND Center and the Department of Radiology
Judy Gichoya, MD
Assistant Professor
Emory University School of Medicine
Measuring Learning Gains in Man-Machine Assemblage When Augmenting Radiology Work with Artificial Intelligence
Abstract
The work setting of the future presents an opportunity for human-technology partnerships, where a harmonious connection between human-technology produces unprecedented productivity gains. A conundrum at this human-technology frontier remains – will humans be augmented by technology or will technology be augmented by humans? We present our work on overcoming the conundrum of human and machine as separate entities and instead, treats them as an assemblage. As groundwork for the harmonious human-technology connection, this assemblage needs to learn to fit synergistically. This learning is called assemblage learning and it will be important for Artificial Intelligence (AI) applications in health care, where diagnostic and treatment decisions augmented by AI will have a direct and significant impact on patient care and outcomes. We describe how learning can be shared between assemblages, such that collective swarms of connected assemblages can be created. Our work is to demonstrate a symbiotic learning assemblage, such that envisioned productivity gains from AI can be achieved without loss of human jobs.
Specifically, we are evaluating the following research questions: Q1: How to develop assemblages, such that human-technology partnerships produce a “good fit” for visually based cognition-oriented tasks in radiology? Q2: What level of training should pre-exist in the individual human (radiologist) and independent machine learning model for human-technology partnerships to thrive? Q3: Which aspects and to what extent does an assemblage learning approach lead to reduced errors, improved accuracy, faster turn-around times, reduced fatigue, improved self-efficacy, and resilience?
Zoom: https://stanford.zoom.us/j/93580829522?pwd=ZVAxTCtEdkEzMWxjSEQwdlp0eThlUT09
PHIND Seminar Series: Serum Modulation of Mitochondrial Function as a Scalable Sensor of Insulin Resistance
Andrew Lipchik, Ph.D.
Postdoctoral Fellow – Michael Snyder Lab
Stanford University
11:00am – 12:00pm Seminar & Discussion
12:00pm – 12:15pm Reception & Light Refreshments
RSVP: https://stanford.zoom.us/webinar/register/7716009863360/WN_dbeuo7csS8q_AhR88XET0g
Location: Zoom
Webinar URL: . https://stanford.zoom.us/s/96358568342
Webinar ID: 963 5856 8342
Dial: +1 650 724 9799 or +1 833 302 1536 (Toll Free)
Password: 767148
ABSTRACT
The global epidemic of obesity is associated with the dramatic increase in the prevalence of type 2 diabetes mellitus (T2D) with an estimated 400 million people worldwide will have T2D by 2030. T2D is proceeded by insulin resistance (IR) for up to decades prior to onset of T2D. Current estimates suggest approximately one in three individuals are sufficiently insulin resistant to be at risk for IR complications including T2D, coronary heart disease and nonalcoholic fatty liver disease. IR often goes undiagnosed due to the complex, invasive and laborious nature of clamp assays preventing their universal application in the clinic. Surrogate measurements using fasting plasma glucose and insulin levels can estimate IR but are imprecise. There is a need for the identification of new biomarkers and assays for the detection and monitoring of IR. Here, we demonstrate the utility of cellular mitochondrial respiration in response to individuals’ serum as a sensor for personalized monitoring of insulin sensitivity. The modulation of insulin-dependent mitochondrial function by patient serum was highly correlated with insulin sensitivity as determined by the gold-standard modified insulin suppression test (IST). We further applied this methodology to monitor insulin sensitivity over time in response to illness as well as treatment with the insulin sensitizing medication, pioglitazone. Our results demonstrate the development and application of a novel surrogate measurement for the determination and monitoring of insulin sensitivity. This assay offers the advantages of minimal invasiveness and complexity compared to IST as well as superior correlation with IST compared to existing surrogate measurements.
ABOUT ANDREW LIPCHIK
Andrew Lipchik majored in Chemistry at Xavier University where he preformed research on the development of oxygen activation Ni(II) complexes with Dr. Craig Davis and Dr. Michael Baldwin at the University of Cincinnati. He went on to obtain his PhD from Purdue University under mentorship of Dr. Laurie Parker. His thesis work focused on identifying determinants of kinase substrate specificity. This understanding was applied to the development of novel kinase-specific peptide biosensors to monitor intracellular kinase activity. Following his graduate work, he joined the laboratory of Michael Snyder at Stanford University where he has focused on understanding the impact of the immune system on insulin resistance and glucose metabolism.
Hosted by: Garry Gold, M.D.
Sponsored by the PHIND Center and the Department of Radiology
PHIND Seminar Series: Identifying Microbiome Markers of Progression of Alzheimer’s Disease
Ami Bhatt, M.D., Ph.D.
Assistant Professor of Medicine (Hematology) and of Genetics
Stanford University
Gavin Sherlock, Ph.D.
Associate Professor of Genetics
Stanford University
11:00am – 12:00pm Seminar & Discussion
RSVP: https://stanford.zoom.us/webinar/register/8016040837299/WN_iBOM7R4XQjOPSb20rkUxbw
Location: Zoom Webinar
Webinar URL: https://stanford.zoom.us/s/99730716280
Webinar ID: 997 3071 6280
Dial: +1 650 724 9799 or +1 833 302 1536 (Toll Free)
Password: 767148
ABOUT AMI BHATT
In perpetual awe of how ‘simple’ microbial organisms can perturb complex, multicellular eukaryotic organisms, Ami Bhatt has chosen to dedicate her research program to inspecting, characterizing and dissecting the microbe-human interface. Nowhere is the interaction between hosts and microbes more potentially impactful than in immunocompromised hosts and global settings where infectious and environmental exposures result in drastic and sometimes fatal health consequences.
Ami’s group identifies problems and questions that arise in the course of routine clinical care. Often in collaboration with investigators at Stanford and beyond, the group applies modern genetic, molecular and computational techniques to seek answers to these questions, better understand host-microbe interactions and decipher how perturbation of these interactions may result in human disease phenotypes.
GAVIN SHERLOCK’S RESEARCH INTERESTS
Adaptive Evolution and the Fitness Landscape: When yeast are evolved under various selective pressures in a chemostat, mutations that arise and provide an adaptive advantage will expand within the population. We have pioneered the use of high throughput sequencing to determine the identity of such mutations, as well as to understand the dynamics of the mutations within the populations, and the interactions between the mutations (such as epistasis). Further, we have developed a DNA barcode based lineage tracking system to determine the distribution of fitness effects (DFE) for newly arising beneficial mutations. We have also characterized what we call the genotype-fitness map for beneficial mutations, and have investigated why beneficial mutations provide a positive fitness effect. We are also interested in how beneficial mutations trade-off for different traits, and how those trade-offs constrain adaptive evolution.
Hosted by: Garry Gold, M.D.
Sponsored by the PHIND Center and the Department of Radiology
Ge Wang, PhD
Clark & Crossan Endowed Chair Professor
Director of the Biomedical Imaging Center
Rensselaer Polytechnic Institute
Troy, New York
Abstract:
AI-based tomography is an important application and a new frontier of machine learning. AI, especially deep learning, has been widely used in computer vision and image analysis, which deal with existing images, improve them, and produce features. Since 2016, deep learning techniques are actively researched for tomography in the context of medicine. Tomographic reconstruction produces images of multi-dimensional structures from externally measured “encoded” data in the form of various transforms (integrals, harmonics, and so on). In this presentation, we provide a general background, highlight representative results, and discuss key issues that need to be addressed in this emerging field.
About:
AI-based X-ray Imaging System (AXIS) lab is led by Dr. Ge Wang, affiliated with the Department of Biomedical Engineering at Rensselaer Polytechnic Institute and the Center for Biotechnology and Interdisciplinary Studies in the Biomedical Imaging Center. AXIS lab focuses on innovation and translation of x-ray computed tomography, optical molecular tomography, multi-scale and multi-modality imaging, and AI/machine learning for image reconstruction and analysis, and has been continuously well funded by federal agencies and leading companies. AXIS group collaborates with Stanford, Harvard, Cornell, MSK, UTSW, Yale, GE, Hologic, and others, to develop theories, methods, software, systems, applications, and workflows.