• 08:15

    DOORS OPEN

  • 08:15

    REGISTRATION & LIGHT BREAKFAST

  • 09:00

    WELCOME

  • 09:15

    AI IN HEALTHCARE: AN INTRODUCTION

  • 09:15

    Improving Efficiency using AI

  • 09:35

    Challenges for Delivering Machine Learning in Health

  • 09:55

    PREDICTIONS

  • 09:55

    AI powered Fertility Treatment

  • 10:15

    Deep Learning for Disease Prediction

  • 10:35

    COFFEE

  • 11:05

    ARTIFICIAL INTELLIGENCE FOR DIAGNOSTICS

  • 11:05
    Saeed Hassanpour

    Artificial Intelligence and Histopathological Characterization of Microscopy Images

    Saeed Hassanpour - Associate Professor - Hassanpour Lab, Geisel School of Medicine, Dartmouth

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    Artificial Intelligence and Histopathological Characterization of Microscopy Images With the recent expansions of whole-slide digital scanning, archiving, and high-throughput tissue banks, the field of digital pathology is primed to benefit significantly from deep learning technology. This talk will cover several applications of deep learning for characterizing histologic patterns on high-resolution microscopy images for cancerous and precancerous lesions. Also, recent advances and future directions for developing and evaluating deep learning models for pathology image analysis will be discussed.

    Dr. Saeed Hassanpour is an Associate Professor in the Departments of Biomedical Data Science, Computer Science, and Epidemiology at Dartmouth College. His research is focused on the use of artificial intelligence in healthcare. Dr. Hassanpour’s research laboratory has built novel machine learning and deep learning models for medical image analysis and clinical text mining to improve diagnosis, prognosis, and personalized therapies. Before joining Dartmouth, he worked as a Research Engineer at Microsoft. Dr. Hassanpour received his Ph.D. in Electrical Engineering with a minor in Biomedical Informatics from Stanford University and a Master of Math in Computer Science from the University of Waterloo in Canada.

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  • 11:25
    James O'Sullivan

    Deep Learning for Audiobased Sociability Assessment in Patients with Neuro-Development Disorders

    James O'Sullivan - Post-Doctoral Researcher - Roche

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    Deep Learning for audio-based sociability assessment in patients with neuro-developmental disorders.

    I was born in Dublin, Ireland. I received my undergraduate degree in Electrical Engineering from UCD, Ireland, my Master's degree in Biomedical Engineering from UCD, and my PhD in Neural Engineering from Trinity College, Ireland. I then moved to New York where I completed a four year postdoc in the lab of Nima Mesgarani at Columbia University. In May 2019 I began a postdoc in Roche, also in New York. In my spare time, I like to go rock climbing and play music.

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  • 11:45
    Krzysztof Jerzy Geras

    Solving Breast Cancer Screening Diagnosis with Deep Learning

    Krzysztof Jerzy Geras - Assistant Professor - NYU School of Medicine

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    Towards solving breast cancer screening diagnosis with deep learning

    Although deep learning has made a stunning progress in the last few years, both in terms of engineering and theory, its real-life applications remain rather limited. One of the fields that has been anticipated to be revolutionized by deep learning for some time, yet proved to be much harder that many expected, is medical diagnosis. In this talk I will shed some light on my 4-year long journey in developing deep learning methods for medical imaging, in particular, for breast cancer screening. I will explain how we created a deep learning model that can perform a diagnosis with an accuracy comparable to experienced radiologists. To achieve this goal we needed a lot of perseverance, novel neural network architectures and training methods specific to medical imaging. I will also discuss the limitations of our work and what can likely be achieved in the next few years.

    Krzysztof is an assistant professor at NYU School of Medicine and an affiliated faculty at NYU Center for Data Science. His main interests are in unsupervised learning with neural networks, model compression, transfer learning, evaluation of machine learning models and applications of these techniques to medical imaging. He previously did a postdoc at NYU with Kyunghyun Cho, a PhD at the University of Edinburgh with Charles Sutton and an MSc as a visiting student at the University of Edinburgh with Amos Storkey. His BSc is from the University of Warsaw. He also did industrial internships in Microsoft Research (Redmond, working with Rich Caruana and Abdel-rahman Mohamed), Amazon (Berlin, Ralf Herbrich's group), Microsoft (Bellevue) and J.P. Morgan (London).

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  • 12:05

    Endoscopic Robotic Surgical AI Tools and Methods

  • 12:25

    LUNCH

  • 13:25

    MEDICAL IMAGING

  • 13:25
    Kyung Sung

    Applications of MRI-based Deep Learning Models to Prostate Cancer Care

    Kyung Sung - Associate Professor of Radiology - University of California, Los Angeles (UCLA)

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    Applications of MRI-based Deep Learning Models to Prostate Cancer Care

    Deep learning (DL) has recently garnered significant attention because of its superior performance in image recognition and classification. One of the central promises of DL is to replace handcrafted imaging features with efficient algorithms for hierarchical feature extraction. We present recently developed magnetic resonance imaging (MRI)-driven deep learning models for improved segmentation, detection, and classification of clinically significant prostatic carcinoma. The presentation will mainly highlight i) unique challenges of MRI data in DL frameworks, ii) construction of DL frameworks through convolutional neural networks (CNNs), and iii) application of the DL framework to the computerized analysis of prostate MRI for improved cancer care.

    Dr. Sung is an Associate Professor of Radiology, where his research primarily focuses on the development of novel medical imaging methods and artificial intelligence using magnetic resonance imaging (MRI). He received a Ph.D. degree in Electrical Engineering from the University of Southern California, Los Angeles, in 2008, and from 2008 to 2012, he finished his postdoctoral training at Stanford in the Departments of Radiology. He joined the University of California, Los Angeles (UCLA) Department of Radiological Sciences, in 2012. His research interest is to develop fast and reliable magnetic resonance imaging (MRI) techniques that can provide improved diagnostic contrast and useful information. In particular, his research group (https://mrrl.ucla.edu/sunglab/) is currently focused on developing advanced deep learning algorithms and quantitative MRI techniques for early diagnosis, treatment guidance, and therapeutic response assessment for oncologic applications. Such developments can offer more robust and reproducible measures of biologic markers associated with human cancers.

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  • 13:45

    Applying CNN's to Medical Imaging

  • 14:05

    COFFEE

  • 14:40

    PLENARY SESSION

  • 14:40
    Brendan Frey

    How AI Discovered a Drug Candidate at Deep Genomics

    Brendan Frey - Co-Founder & CEO, & Professor - Deep Genomics & University of Toronto

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    How Deep Learning is Transforming Drug Discovery

    Brendan Frey, CEO and Founder of Deep Genomics, will explain how AI did most of the heavy lifting in obtaining the company's first therapeutic candidate. This included discovering novel biology, designing novel compounds, prioritizing compounds by predicted potency and toxicity, creating animal models, designing animal studies and designing the clinical trial. Their AI technology is enabling Deep Genomics to explore an expanding universe of genetic therapies, and to advance novel drug candidates more rapidly and with a higher rate of success than was previously possible.

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  • 15:20

    COMPLIANCE & ETHICS

  • 15:20

    Scalable and Accurate Machine Learning with Electronic Records

  • 15:40

    PANEL: The Future of AI in the Health & Pharmaceutical Industry

  • 16:00

    CONVERSATION & DRINKS

  • 17:00

    END OF DAY 1

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  • 08:15
    Salim Samanani

    Using Machine Learning to Predict 30-Day Risk of Overdose Hospitalization, Emergency Visit or Death Among Albertans Who Receive a Dispensation of Prescription Opioids

    Salim Samanani - Founder and Medical Director - OKAKI

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    Prescribed opioids are a key factor in driving the current opioid crisis in Alberta (AB), a province of Canada, and nationwide. Over 400,000 patients a year currently receive at least one opioid prescription in AB, out of a population of 4.5 million. Currently, no risk calculators are available that quantify the risk of hospitalization or death following a dispensation of prescribed opioids and prescribers must rely on guidelines to assess risk. OKAKI is a public health informatics social enterprise that manages Alberta’s prescription monitoring program. OKAKI and its partners undertook a study to use machine learning (ML) with the province’s large health administrative datasets to develop and test a model to predict the 30-day risk of hospitalization, emergency department visit (ED) or death, at the time of an opioid dispensation. A model that performed better than current approaches could potentially be used as a clinical decision aid or to support province-wide monitoring and interventions. The presentation will summarize the study context, methods and results, as well as key challenges in implementing this type of prediction model in clinical or public health practice.

    Dr. Samanani is a physician executive with more than 20 years of clinical, public health, and health system analytics experience. In 2008, he founded OKAKI (Blackfoot meaning "be wise"), a public health informatics social enterprise, headquartered in Alberta, Canada. For over a decade, OKAKI has leveraged IT and data to help address challenges in indigenous health, environmental health, emergency and disaster response, immunization, chronic disease and prescription monitoring. In 2019, OKAKI and its partners launched a precision public health initiative, with an initial focus on predicting and reducing opioid overdoses.

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  • 08:35

    DOORS OPEN

  • 08:35

    REGISTRATION & LIGHT BREAKFAST

  • 09:20

    WELCOME

  • 09:35

    START UP SESSION

  • 09:35

    Wearables for Epilepsy Monitoring

  • 09:55

    AI Powered Chatbots to Combat Substance Abuse

  • 10:15

    Dental CAD/CAM Automation with Deep Learning

  • 10:35

    COFFEE

  • 11:05

    AI FOR EFFECTIVE TREATMENT

  • 11:05
    Maha Reda Farhat

    ML to Genomic Data in Predicting Antibiotic Resistance in Bacteria

    Maha Reda Farhat - Assistant Professor - Department of Biomedical informatics, Harvard Medical School

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    Maha Farhat holds an MD from the McGill University Faculty of Medicine and a MSc in biostatistics from the Harvard Chan School of Public Health. She is also a practicing physician at the Massachusetts General Hospital Division of Pulmonary and Critical Care Medicine.

    Dr. Farhat's research focuses on the development and application of methods for associating genotype and phenotype in infectious disease pathogens, with a strong emphasis on translation to better diagnostics and surveillance in resource-poor settings. To date, Farhat's work has focused on the pathogen Mycobacterium tuberculosis and spans the spectrum from computational analysis to field studies. She is PI and Co-Investigator on several large projects funded by NIH including the NIAID and the BD2K initiative.

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  • 11:25

    Emotionally Intelligence NLP for Patient-Centred Care

  • 11:45

    Using Surgical Robotics for Remote Surgery

  • 12:05

    AI Powered Chatbots for Treatment of Mental Health Conditions

  • 12:25

    HEALTH & WELLNESS

  • 12:25

    Using AI to Understand Patterns in Workplace Wellness

  • 12:45

    LUNCH

  • 13:40
    Jessie Li

    Transfer learning for the prediction of atrial fibrillation and sleep apnea

    Jessie Li - Global Head of Data Science - Jawbone Health

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    Transfer learning for the prediction of atrial fibrillation and sleep apnea

    Inexpensive and highly portable medical sensing technology has the potential to deliver savings for healthcare providers, through screening and early detection. However, the data necessary to support machine learning models on these newly developed sensor modalities - sensor streams together with time-aligned, expert-annotated labels - is often difficult and expensive to obtain. By contrast, high-quality data suitable for machine learning on better-established modalities, such as electrocardiogram (ECG), is abundant and often free. We propose a method allowing for the use of such easy-to-come-by data in building models on photoplethysmography (PPG) sensor modalities. With this method, data requirements are reduced to streams of PPG data time-aligned with readings from ECG together with labelled outcome for ECG. We demonstrate the method by building models for atrial fibrillation and sleep apnea based on data from a wrist-worn PPG sensor, with the only labels coming from publicly available ECG data. We find that the models developed using the transfer learning approach outperform models trained directly on the PPG sensor and are competitive with state-of-the-art ECG-based models.

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  • 14:00

    PUPLIC HEALTH

  • 14:00
    Margarita Sordo

    Evaluating the Impact of Determinants of Health

    Margarita Sordo - Senior Medical Informatician - Brigham and Women's Hospital, Massachusetts General Hospital

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    Evaluating the Impact of Determinants of Health

    There are known individual, social, economic, medical and environmental factors, known as determinants of health (DOH)1,2 that influence overall wellbeing, and physiological decline. Identification and evaluation of this impact on wellbeing may provide insights for early prevention and intervention to, not only as an adjuvant to health, but to prevent and reduce poor outcomes associated with physiological decline. We aim to identify and assess interrelated conditions and factors that influence health and wellbeing of population over time. Agent-Based Models (ABM)3, an Artificial Intelligence technique provides the mechanisms for dynamically modelling the impact of DOH on the general population to gather insights into the cumulative effects of DOH on the overall wellbeing over time.

    Dr. Sordo is a Senior Medical Informatician at the Brigham and Women’s Hospital and Massachusetts General Hospital, and an Instructor of General Internal Medicine and Primary Care at Harvard Medical School. Her research and scholarly writing include elicitation and representation of clinical knowledge, artificial intelligence and machine learning techniques in medicine and healthcare to further advance clinical decision support. Current work focuses on the application of complex adaptive systems to evaluate the impact of determinants of health and public health policies on individual health.

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  • 14:20

    PANEL: Trusting AI: How Can we ensure a 'Do No Harm' Policy When Applying AI to Healthcare?

  • 15:00

    END SUMMIT

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RE•WORK Boston Summit

RE•WORK Boston Summit

18 - 19 June 2020

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