SPEAKERS

JUNE 18 TUESDAY
 
Sir Michael Brady FRS FREng FMedsci

Emeritus Professor of Oncological Imaging, University of Oxford

Executive Chairman: National Consortium on Intelligent Medical Imaging

Founder Chairman: Perspectum Diagnostics, ScreenPoint Medical

Founder Director: Volpara Health Technologies, Mirada Medical

Chairman: Optellum

(Membre Étranger de l’Académie des Sciences)

Title:

Quantitative and Intelligent Imaging for Clinical Decision Support

 

Abstract:

We discuss a number of important developments in CARS primarily by reference to innovations in some of the medical image analysis companies of which I am a Founder.  First, image analysis can be quantitative, each pixel measuring a physical quantity.  We first illustrate this by quantitative MRI of the liver, measuring proton density fat fraction; iron content; and fibroinflammation (units of time).  This is applied to (non-alcoholic) fatty liver disease, steatohepatitis (NASH), and therapeutic interventions, both measuring the effect of anti-NASH drugs and supporting liver surgery.  Then, we show how breast density may be measured and applied to estimate x-ray dose in mammography.  Second, image analysis can be intelligent based on methods developed in AI and Machine Learning (a branch of AI). We illustrate this both in MRI analysis of the liver and in a decision support system for mammography.  This offers opportunities in work flow and we show how the combination of all radiologists working with Transpara decision support software can out-perform either working individually.  Finally, we discuss some of the strengths and limitations of machine learning applied to medical imaging.

 
Dr. Bibb Allen Jr., MD, FACR

Chief Medical Officer

American College of Radiology Data Science Institute

Diagnostic Radiology

Grandview Medical Center

Birmingham, Alabama USA

 

Title:

Fostering A Strong Ecosystem For Artificial Intelligence In Medical Imaging

 

Abstract:

Fueled by the ever-increasing amount of data generated by the healthcare system applications for artificial intelligence in healthcare, especially within diagnostic imaging, are rapidly proliferating. Currently, no well-defined framework exists for determining how great ideas for AI algorithms in healthcare will advance from development to integrated clinical practice. Healthcare stakeholders including physicians, patients, medical societies, hospital systems, software developers, the health information technology industry and governmental regulatory agencies all comprise a community that will need to function as an ecosystem system in order for AI algorithms to be deployed, monitored, and improved in widespread clinical practice. Radiologists can play an important role in promoting this AI ecosystem by delineating structured AI use cases for diagnostic imaging and standardizing data elements and workflow integration interfaces. By developing structured AI use cases based on the needs of the physician community, radiologists and radiology specialty societies can assist developers in creating the tools that will advance the practice of medicine. If these use cases specify how datasets for algorithm training, testing and validation can be developed as well as specifying parameters for clinical integration and pathways for assessing algorithm performance in clinical practice, the likelihood of bringing safe and effective algorithms to clinical practice will increase dramatically. The development of an active AI ecosystem will facilitate the development and deployment of AI tools for healthcare that will help physicians solve medicine’s important problems.

 
Leo Joskowicz
PhD

Detection and grading of sacroiliitis in computed tomography as incidental finding using deep learning

TITLE:

Amber Simpson
PhD

Radiomics and radiogenomics: clinical applications

TITLE:

Kensaku Mori
PhD

Results of the national Japanese project on Multi-disciplinary computational anatomy

TITLE:

Yoshihiro Muragaki
MD, PhD

TITLE:

Smart Cyber Operating Theater (SCOT) realized through Internet of Things (IoT)

Sylvain Gioux
PhD

TITLE:

QuantSURG : Making Sense in Surgery using Near-Infrared Optical Imaging

Lena Maier-Hein
PhD

COMBIOSCOPY: Computational biophotonics in endoscopy

TITLE:

Parvin Mousavi
PhD

MD+Machine: Machine Learning for Computer Aided Diagnosis and Interventions in Prostate Cancer

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Masaaki Ito
MD

Laparoscopic video image analysis: automated annotation with artificial intelligence

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Krishna Kandarpa
MD, PhD

Artificial intelligence in medical imaging: perspective from the NIH

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Giuseppe Esposito
MD

Artificial intelligence in nuclear medicine

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Claire Haegelen
PhD, MD

TITLE:

Surgical data science for functional neurosurgery

Guang-Zhong Yang
PhD

TITLE:

Cathbot - Navigation and Control for Endovascular Intervention

Microsurgical robot and its evaluation using bionic humanoids

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Kanako Harada
PhD
Paul Laforet
MD

TITLE:

Medical care and hospitalization in extreme environments like Antarctica (or space)

Adrien 
Desjardins
PhD

TITLE:

Molecular photoacoustic imaging during ultrasound-guided interventions

Hubertus Feußner
MD

 Surgery 4.0: On the threshold to a new type of surgery.

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Yoshiharu Ohno
MD, PhD

TITLE:

Artificial intelligence in CAD:

clinical perspective

Charles-Karim Bensalah
PhD, MD

TITLE:

Robotics and Surgical data science in urology

D. Louis Collins
PhD

TITLE:

Recent progress in Medical Image Processing and Deep Learning

Ulrich Straube
MD

TITLE:

Medical Care for Manned Deep Space Exploration - What is needed?

Thomas Neumuth
PhD

TITLE:

ORNET and beyond: AI and machine learning in the OR of the future

Dan Elson
PhD

Optical theranostics: image-guided cancer thermal therapy using light

TITLE:

Xavier Pennec
PhD

Geometric Statistics for

Computational Anatomy

TITLE:

The Challenge for Ai in Medical Imaging in China

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Huimao Zhang
MD, PhD

Potential Applications of Artificial Intelligence in Surgery

Dirk Wilhelm
MD

TITLE:

Mickael Tanter
PhD

Ultrafast imaging and Superresolution Ultrasound for Medicine

TITLE:

Silvana Perretta
PhD, MD

The future of surgery is flexible and image guided

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Richard Satava
MD FACS

TITLE:

The Future of Image Guided Surgery

Nassir Navab
PhD

TITLE:

Robotic Imaging, Machine Learning and Augmented Reality for Computer Assisted Interventions

Hongen Liao
PhD

Innovation of 3D Imaging and Visualization for Intelligent Minimally Invasive Surgery

TITLE:

Pedro Alvarez Diaz
MD, PhD

TITLE:

New Horizons in orthopedic surgery: Past, present and future perspectives

Guoyan Zheng
PhD

Probabilistic Deep Voxelwise Dilated Residual Networks for Whole Heart Segmentation

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François Rousseau
PhD

TITLE:

MultiAtlas Neonatal Brain MRI Segmentation: From Patch-based to Deep Approaches

Christos Angelopoulos
DDS

Future of Oral and

Maxillofacial Radiology

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Carla Pugh
MD, PhD

Use of Sensor-Based Metrics to Improve Mastery Learning During Simulation

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Eric vanSonnenberg
MD

TITLE:

Preparing students and practitioners for artificial intelligence: imperatives in medical education

Leonard Berliner
MD

Artificial Intelligence in clinical practice: success and challenges

TITLE:

Yoshihiko Hayakawa
PhD

TITLE:

3D Modeling and Virtual Reality in Oral and Maxillofacial Region

Arnaud Runge

Examples of Technology transfer - how to get involved

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