Calendar

Aug
17
Tue
2021
PHIND Seminar – Orestis Vardoulis, Ph.D. @ Zoom - See Description for Zoom Link
Aug 17 @ 11:00 am – 12:00 pm
PHIND Seminar - Orestis Vardoulis, Ph.D. @ Zoom - See Description for Zoom Link

PHIND Seminar Series: Peace of mind for those affected by stroke

Orestis Vardoulis, Ph.D.
Co-Founder & CEO
ZeitMedical

 

Zoom Webinar Details
Webinar URL: https://stanford.zoom.us/s/94427469356
Dial: US: +1 650 724 9799  or +1 833 302 1536 (Toll Free)
Webinar ID: 944 2746 9356
Passcode: 999031

11:00am – 12:00pm Seminar & Discussion
12:00pm – 12:15pm Reception
RSVP Here

 

ABSTRACT

There is a growing population of over 10 million Americans that live with an elevated risk of having a stroke.

Each year approximately 1 million Americans survive a stroke or a ministroke, often severely affected by its debilitating effects. A more disabling stroke frequently occurs after the seminal events, leaving patients and their families scarred for life.

TIME = BRAIN. Early hospital presentation is the most critical determinant in good stroke outcomes. However, most patients arrive at the hospital often hours after the event, with less than 10% receiving any form of treatment (thrombolysis / thrombectomy).

As a result, at risk individuals struggle daily with the fear, a stroke might happen during night-time or when they are alone. Unfortunately a stroke that goes unnoticed for hours, is most often not treatable due to the lack of salvageable tissue.

To alleviate that fear, we are creating an AI-powered, smart-headband that analyzes brain waves to detect the onset of an event immediately, and alert the patient, caregivers and 911.

Our stroke detection AI has already been shown to detect ischemia during high-risk surgeries with 90% sensitivity and no false positives.

We have received FDA breakthrough designation for our solution and are currently running a pilot human factors and signal quality study.

Our vision is to provide peace of mind and optimal brain health for everyone.

 

ABOUT
Orestis is the CEO and Co-founder of Zeit Medical, a telehealth company that offers at home monitoring and alert solutions for patients at risk for stroke. Prior to starting Zeit, Orestis was a Stanford Biodesign Innovation Fellow where his team developed the initial idea about at-home stroke detection. Orestis trained as a Mechanical Engineer, at Aristotle University, Greece, earned his PhD in Biotechnology and Bioengineering at EPFL, Switzerland and conducted cutting edge research in flexible wearable electronics with the Bao Group at Stanford Chemical Engineering. He has authored more than twenty publications in prestigious journals and has filed for a variety of patents at the intersection of materials technology and medical devices. Orestis currently lives in San Francisco, where he also contributes to the UCSF-Stanford pediatric device consortium as a technology advisor.  He also maintains close ties with the med-tech and health-tech communities in Switzerland and Greece, contributing to regional Biodesign educational workshops.

 

Hosted by: Garry Gold, M.D.
Sponsored by the PHIND Center and the Department of Radiology

Sep
21
Tue
2021
PHIND Seminar – Sindy KY Tang, Ph.D. @ Zoom - See Description for Zoom Link
Sep 21 @ 11:00 am – 12:00 pm
PHIND Seminar - Sindy KY Tang, Ph.D. @ Zoom - See Description for Zoom Link

PHIND Seminar Series: Towards precision diagnostic and prediction of food allergy

Sindy KY Tang, Ph.D.
Associate Professor of Mechanical Engineering, Senior Fellow at the Woods Institute for the Environment and Professor, by courtesy, of Radiology – PHIND Center
Stanford University

 

Location: Zoom
Webinar URL: https://stanford.zoom.us/s/91932966334
Dial: US: +1 650 724 9799  or +1 833 302 1536 (Toll Free)
Webinar ID: 919 3296 6334
Passcode: 383071

11:00am – 12:00pm Seminar & Discussion
RSVP Here

 

ABSTRACT
Food allergy has reached epidemic proportions. Accurate in vitro methods that are efficient and easy to use to identify offending food allergens are lacking. Oral food challenge, the gold standard for food allergy assessment, is often not performed as it places the patient at risk of anaphylaxis. As such, food allergy is often identified only after an adverse reaction that could be life-threatening. Our long-term goal is to develop a food allergy diagnostic test that is accurate, safe, rapid, and accessible, so that food allergy can be easily identified prior to the occurrence of an adverse reaction, and that the efficacy of immunotherapy for food allergy can be tracked more effectively. This talk will discuss our recent work on developing such a test. Our approach is based on the Basophil Activation Test (BAT), which measures the activation of basophils in whole blood after stimulation with specific food allergens ex vivo. The BAT has been shown to be highly predictive of allergic reactions. However, the need for flow cytometry has limited its broader use. We are developing a miniaturized, standalone version of the BAT. We envision that the test can be used at the point of care, such as the doctor’s office or at a local pharmacy.

 

ABOUT
Prof. Sindy KY Tang is the Kenneth and Barbara Oshman Faculty Scholar and Associate Professor of Mechanical Engineering and by courtesy of Radiology (Precision Health and Integrated Diagnostics) at Stanford University. She received her Ph.D. from Harvard University in Engineering Sciences under the supervision of Prof. George Whitesides. Her lab at Stanford works on the fundamental understanding of fluid mechanics and mass transport in micro-nano systems, and the application of this knowledge towards problems in biology, rapid diagnostics for health and environmental sustainability. The current areas of focus include the flow physics of confined micro-droplets using experimental and machine learning methods, interfacial mass transport and self-assembly, and ultrahigh throughput opto-microfluidic systems for disease diagnostics, water and energy sustainability, and single-cell wound healing studies. She was a Stanford Biodesign Faculty Fellow in 2018. Dr. Tang’s work has been recognized by multiple awards including the NSF CAREER Award, 3M Nontenured Faculty Award, the ACS Petroleum Fund New Investigator Award, and invited lecture at the Nobel Symposium on Microfluidics in Sweden. Website: http://web.stanford.edu/group/tanglab/

 

Hosted by: Garry Gold, M.D.
Sponsored by the PHIND Center and the Department of Radiology

Oct
6
Wed
2021
Early Detection of Cancer Conference @ Virtual Event
Oct 6 – Oct 8 all-day
Early Detection of Cancer Conference @ Virtual Event

Cancer Research UK, OHSU Knight Cancer Institute and the Canary Center at Stanford, present the Early Detection of Cancer Conference series. The annual Conference brings together experts in early detection from multiple disciplines to share ground breaking research and progress in the field.

The Conference is part of a long-term commitment to invest in early detection research, to understand the biology behind early stage cancers, find new detection and screening methods, and enhance uptake and accuracy of screening.

The 2021 conference will take place October 6-8 virtuallyFor more information visit the website: http://earlydetectionresearch.com/

Oct
12
Tue
2021
Cancer Early Detection Seminar Series – Azra Raza, MD @ Venue coming soon!
Oct 12 @ 11:00 am – 12:00 pm
Cancer Early Detection Seminar Series - Azra Raza, MD @ Venue coming soon!

CEDSS: The First Cell: A new model for cancer research and treatment

Azra Raza, M.D.
Chan Soon-Shiong Professor of Medicine
Director, Myelodysplastic Syndrome Center
Columbia University Medical Center

 

Location: Zoom
Meeting URL: https://stanford.zoom.us/s/99340345860
Dial: US: +1 650 724 9799  or +1 833 302 1536 (Toll Free)
Meeting ID: 993 4034 5860
Passcode: 711508

RSVP Here

 

ABSTRACT

Cancer research continues to be predicated on a 1970’s model of research and treatment. Despite half a century of intense research, we are failing spectacularly to improve the outcome for patients with advanced disease. Those who are cured continue to be treated mostly with the older strategies (surgery-chemo-radiation). Our contention is that the real solution to the cancer problem is to diagnose cancer early, at the stage of The First Cell. The rapidly evolving technologies are doing much in this area but need to be expanded. We study a pre-leukemic condition called myelodysplastic syndrome (MDS) with the hope that we can detect the first leukemia cells as the disease transforms to acute myeloid leukemia (AML). Towards this end, we have collected blood and bone marrow samples on MDS and AML patients since 1984. Today, our Tissue Repository has more than 60,000 samples. We propose novel methods to identify surrogate markers that can identify the First Cell through studying the serial samples of patients who evolve from MDS to AML.

 

ABOUT

Dr. Raza is a Professor of Medicine and Director of the MDS Center at Columbia University in New York, NY.She started her research in Myelodisplastic Syndromes (MDS) in 1982 and moved to Rush University, Chicago, Illinois in 1992, where she was the Charles Arthur Weaver Professor in Oncology and Director, Division of Myeloid Diseases. The MDS Program, along with a Tissue Repository containing more than 50,000 samples from MDS and acute leukemia patients was successfully relocated to the University of Massachusetts in 2004 and to Columbia University in 2010.

Before moving to New York, Dr. Raza was the Chief of Hematology Oncology and the Gladys Smith Martin Professor of Oncology at the University of Massachussetts in Worcester. She has published the results of her laboratory research and clinical trials in prestigious, peer reviewed journals such as The New England Journal of Medicine, Nature, Blood, Cancer, Cancer Research, British Journal of Hematology, Leukemia, and Leukemia Research. Dr. Raza serves on numerous national and international panels as a reviewer, consultant and advisor and is the recipient of a number of awards.

 

Hosted by: Utkan Demirci, Ph.D.
Spon
sored by: The Canary Center & the Department of Radiology 
Stanford University – School of Medicine

Oct
19
Tue
2021
PHIND Seminar – Christina Curtis, Ph.D. @ Venue coming soon!
Oct 19 @ 11:00 am – 12:00 pm
PHIND Seminar - Christina Curtis, Ph.D. @ Venue coming soon!

PHIND Seminar Series: Topic TBA

Christina Curtis, Ph.D.
Associate Professor of Medicine (Oncology) and of Genetics
Stanford University

 

Location: Venue coming soon!
11:00am – 12:00pm Seminar & Discussion
12:00pm – 12:15pm Reception
RSVP coming soon!

 

ABSTRACT
Coming soon!

 

ABOUT
Coming soon!

 

Hosted by: Garry Gold, M.D.
Sponsored by the PHIND Center and the Department of Radiology

Nov
16
Tue
2021
PHIND Seminar – Michael Eisenberg, M.D. & Gary M. Shaw, Ph.D. @ Venue coming soon!
Nov 16 @ 11:00 am – 12:00 pm
PHIND Seminar - Michael Eisenberg, M.D. & Gary M. Shaw, Ph.D. @ Venue coming soon!

PHIND Seminar Series: Male Infertility and the Future Risk of Vascular and CV Disease

Michael Eisenberg, M.D.
Associate Professor of Urology and, by courtesy, of Obstetrics and Gynecology
Stanford University Medical Center

 

Gary M. Shaw, Ph.D.
NICU Nurses Professor and Professor, by courtesy, of Health Research and Policy (Epidemiology) and of Obstetrics and Gynecology (Maternal Fetal Medicine)
Stanford University

 

Location: Venue coming soon!
11:00am – 12:00pm Seminar & Discussion
12:00pm – 12:15pm Reception
RSVP coming soon!

 

ABSTRACT
Coming soon!

 

ABOUT
Coming soon!

 

Hosted by: Garry Gold, M.D.
Sponsored by the PHIND Center and the Department of Radiology

Apr
17
Wed
2024
IBIIS & AIMI Seminar: Building Fair and Trustworthy AI for Healthcare @ Clark Center S360 - Zoom Details on IBIIS website
Apr 17 @ 12:00 pm – 1:00 pm

Roxana Daneshjou, MD, PhD
Assistant Professor, Biomedical Data Science & Dermatology
Assistant Director, Center of Excellence for Precision Heath & Pharmacogenomics
Director of Informatics, Stanford Skin Innovation and Interventional Research Group
Stanford University

Title: Building Fair and Trustworthy AI for Healthcare

Abstract: AI for healthcare has the potential to revolutionize how we practice medicine. However, to do this in a fair and trustworthy manner requires special attention to how AI models work and their potential biases. In this talk, I will cover the considerations for building AI systems that improve healthcare.

May
22
Wed
2024
IBIIS & AIMI Seminar: Facilitating Patient and Clinician Value Considerations into AI for Precision Medicine @ Clark Center S360 - Zoom Details on IBIIS website
May 22 @ 11:00 am – 12:00 pm

Mildred Cho, PhD
Professor of Pediatrics, Center of Biomedical Ethics
Professor of Medicine, Primary Care and Population Health
Stanford University

Title: Facilitating Patient and Clinician Value Considerations into AI for Precision Medicine

Abstract:
For the development of ethical machine learning (ML) for precision medicine, it is essential to understand how values play into the decision-making process of developers. We conducted five group design exercises with four developer participants each (N=20) who were asked to discuss and record their design considerations in a series of three hypothetical scenarios involving the design of a tool to predict progression to diabetes. In each group, the scenario was first presented as a research project, then as development of a clinical tool for a health care system, and finally as development of a clinical tool for their own health care system. Throughout, developers documented their process considerations using a virtual collaborative whiteboard platform. Our results suggest that developers more often considered client or user perspectives after changing the context of the scenario from research to a tool for a large healthcare setting. Furthermore, developers were more likely to express concerns arising from the patient perspective and societal and ethical issues such as protection of privacy after imagining themselves as patients in the health care system. Qualitative and quantitative data analysis also revealed that developers made reflective/reflexive statements more often in the third round of the design activity (44 times) than in the first (2) or second (6) rounds. These statements included statements on how the activity connected to their real-life work, what they could take away from the exercises and integrate into actual practice, and commentary on being patients within a health care system using AI. These findings suggest that ML developers can be encouraged to link the consequences of their actions to design choices by encouraging “empathy work” that directs them to take perspectives of specific stakeholder groups. This research could inform the creation of educational resources and exercises for developers to better align daily practices with stakeholder values and ethical ML design.

Jun
24
Mon
2024
IBIIS & AIMI Seminar: Deepening Collaboration with Stanford & Pennsylvania, Toward Developing Joint Strategies to Close the ‘Cancer Care’ & ‘Clinical Trial Volume’ Gap in LMICs @ Clark Center S360 - Zoom Details on IBIIS website
Jun 24 @ 12:30 pm – 1:30 pm

Ifeoma Okoye MBBS, FWACS, FMCR 
Professor of Radiology and Director
University of Nigeria Centre for Clinical Trials
College of Medicine, University of Nigeria

Title: Deepening Collaboration with Stanford & Pennsylvania, Toward Developing Joint Strategies to Close the ‘Cancer Care’ & ‘Clinical Trial Volume’ Gap in LMICs

Abstract
In this seminar I will be addressing the dire cancer survival outcomes in low- and middle-income countries (LMICs), with a particular focus on Sub-Saharan Africa. Cancer survival rates in Sub-Saharan Africa are alarmingly low. According to the World Health Organization, cancer deaths in LMICs account for approximately 70% of global cancer fatalities. In Nigeria, the five-year survival rate for breast cancer, one of the most common cancers, stands at a disheartening 10-30%, compared to over 80% in high-income countries. This stark disparity highlights the urgent need for sustained comprehensive cancer interventions in our region.

Here, I will discuss the pivotal role in the cancer control sphere, of a new software, ONCOSEEK, capable of early detecting 11 types of Cancers! It’s particular emphasis on the Patient Perspective, which aligns with our ethos of need for holistic patient care. In addition I will discuss recent developments on collaborative effort with the Gevaert lab at Stanford University and the University of Pennsylvania.

Sep
18
Wed
2024
IBIIS & AIMI Seminar – “GREEN: Generative Radiology Report Evaluation and Error Notation” & ” Leveraging Patch-Level Representation Learning with Vision Transformer for Prostate Cancer Foundation Models” @ Clark Center S360 - Zoom Details on IBIIS website
Sep 18 @ 12:00 pm – 1:00 pm
Sophie Ostmeier

Sophie Ostmeier, MD
Postdoctoral Scholar
Department of Radiology
Stanford School of Medicine

Title: GREEN: Generative Radiology Report Evaluation and Error Notation

Abstract
Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images. Existing automatic evaluation metrics either suffer from failing to consider factual correctness (e.g., BLEU and ROUGE) or are limited in their interpretability (e.g., F1CheXpert and F1RadGraph). In this paper, we introduce GREEN (Generative Radiology Report Evaluation and Error Notation), a radiology report generation metric that leverages the natural language understanding of language models to identify and explain clinically significant errors in candidate reports, both quantitatively and qualitatively. Compared to current metrics, GREEN offers: 1) a score aligned with expert preferences, 2) human interpretable explanations of clinically significant errors, enabling feedback loops with end-users, and 3) a lightweight open-source method that reaches the performance of commercial counterparts. We validate our GREEN metric by comparing it to GPT-4, as well as to error counts of 6 experts and preferences of 2 experts. Our method demonstrates not only higher correlation with expert error counts, but simultaneously higher alignment with expert preferences when compared to previous approaches.

Jeong Hoon Lee

Jeong Hoon Lee, PhD
Postdoctoral Researcher
Department of Radiology
Stanford School of Medicine

Title: Leveraging Patch-Level Representation Learning with Vision Transformer for Prostate Cancer Foundation Models

Abstract:
Recent advancements in self-supervised learning (SSL), emerging as an effective approach for imaging foundation models, enable the effective pretraining of AI models across multiple domains without the need for labels. Despite the rapid advancements, their application in medical imaging remains challenging due to the subtle difference between cancer and normal tissue. To address this limitation, in this study, we propose an AI architecture ProViCNet that employs the vision transformer (ViT) based segmentation architecture with patch-level contrastive learning for better feature representation. We validated our model in prostate cancer detection tasks using three types of magnetic resonance imaging (MRI) across multiple centers. To evaluate the performance of feature representation in this model, we performed downstream tasks with respect to Gleason grade score and race prediction. Our model demonstrated significant performance improvements compared to the state-of-the-art segmentation architectures. This study proposes a novel approach to developing foundation models for prostate cancer imaging overcoming SSL limitations.