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What: Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions

Why: To reduce academia’s carbon footprint, accommodate the schedules of the world’s top scientists and maintain social distancing

Who: All are welcome!

Where: Zoom Webinar

Recordings of previous talks can be found here

We are currently inviting speakers for Fall 2024! If you are interested or would like to nominate a speaker, please fill out this nomination form

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David Ouyang, MD
WhenFriday, May 10th, 2024 11:00 am - 12:00 am
Title:  Blinded Prospective Randomized Trials of AI in Echocardiography

Abstract: Artificial intelligence (AI) has been developed for echocardiography, although not yet tested with blinding and randomization. To evaluate impact of AI in the interpretation workflow, we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov NCT05140642, no outside funding) of AI vs. sonographer initial assessment of left ventricular ejection fraction (LVEF). The primary endpoint was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (>5% change). From 3769 echocardiographic studies screened, 274 studies were excluded due to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference - 10.4%, 95% CI -13.2% to -7.7%, P<0.001 for noninferiority, P<0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent prior cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference -0.96%, 95% CI -1.34% to -0.54%, P<0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between AI vs. sonographer’s initial assessments (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was noninferior to assessment by sonographer

Bio: David Ouyang is a cardiologist and researcher in the Department of Cardiology and Division of Artificial Intelligence in Medicine at Cedars-Sinai Medical Center. As a physician-scientist and statistician with focus on cardiology and cardiovascular imaging, he works on applications of deep learning, computer vision, and the statistical analysis of large datasets within cardiovascular medicine. As an echocardiographer, he works on applying deep learning for precision phenotyping in cardiac ultrasound and the deployment and clinical trials of AI models. He majored in statistics at Rice University, obtained his MD at UCSF, and received post-graduate medical education in internal medicine, cardiology, and a postdoc in computer science and biomedical data science at Stanford University. His group works on multi-modal datasets, linking EHR, ECG, echo, and MRI data for a broad perspective on cardiovascular disease and have diverse backgrounds (ranging from physics, mechanical engineering, computer science to cardiology, anesthesia, and internal medicine).

Stephanie Hyland, PhD
When: Friday, September 13th, 2024, 10am-11am EST
Title:  TBD

Abstract: TBD

BioTBD

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Anant Madabhushi, PhD
When: Friday, September 27th, 2024, 10am-11am EST
Title:  Sequence-based deep learning models for understanding gene regulation and disease genetics

Abstract: 

Traditional biology generally looks at only a few aspects of an organism at a time and

attempts to molecularly dissect diseases and study them part by part with the hope that the sum of knowledge of parts would help explain the operation of the whole. Rarely has this been a successful strategy to understand the causes and cures for complex diseases. The motivation

for a systems based approach to disease understanding aims to understand how large numbers of interrelated health variables, gene expression profiling, its cellular architecture and

microenvironment, as seen in its histological image features, its 3 dimensional tissue

architecture and vascularization, as seen in dynamic contrast enhanced (DCE) MRI, and its

metabolic features, as seen by Magnetic Resonance Spectroscopy (MRS) or Positron Emission

Tomography (PET), result in emergence of definable phenotypes. Within our group has been

developing novel computerized knowledge alignment, representation, and fusion tools for

integrating and correlating heterogeneous biological data spanning different spatial and

temporal scales, modalities, and functionalities. These tools include computerized feature

analysis methods for extracting subvisual attributes for characterizing disease appearance and

behavior on radiographic (radiomics) and digitized pathology images (pathomics). In this talk I

will discuss the development work in our group on new radiomic and pathomic approaches for

capturing intra-tumoral heterogeneity and modeling tumor appearance. I will also focus my talk on how these radiomic and pathomic approaches can be applied to predicting disease outcome, recurrence, progression and response to therapy in the context of prostate, brain, rectal, oropharyngeal, and lung cancers. Additionally I will also discuss some recent work on looking at use of pathomics in the context of racial health disparity and creation of more precise and tailored prognostic and response prediction models.

Bio:

Dr. Anant Madabhushi is the Robert W Woodruff Professor of Biomedical Engineering; and on the faculty in the Departments of Pathology, Biomedical Informatics, and Radiology and Imaging Sciences at Emory University. He is also a Research Health Scientist at the Atlanta Veterans Administration Medical Center. Dr. Madabhushi has authored more than 475 peer-reviewed publications and more than 100 patents issued or pending. He is a, Fellow of the American Institute of Medical and Biological Engineering (AIMBE), and the Institute for Electrical and Electronic Engineers (IEEE) and the National Academy of Inventors (NAI). His work on "Smart Imaging Computers for Identifying lung cancer patients who need chemotherapy" was called out by Prevention Magazine as one of the top 10 medical breakthroughs of 2018. In 2019, Nature Magazine hailed him as one of 5 scientists developing "offbeat and innovative approaches for cancer research". Dr. Madabhushi was named to The Pathologist’s Power List in 2019, 2020, 2021 and 2022.

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