Revolutionizing Radiology with Deep Learning: The Road to RSNA 2017

Revolutionizing Radiology with Deep Learning: The Road to RSNA 2017

• 1.
  The Road toRSNA 2017 REVOLUTIONIZING RADIOLOGY WITH DEEP LEARNING
• 2.
  Medical image analysisis one of the world’s fastest growing markets, with annual revenue in healthcare alone increasing to $1.523 billion worldwide in 2025 from less than $100,000 last year, according to Tractica. Source: https://blogs.nvidia.com/blog/2017/09/11/medical-imaging-at-miccai/
• 3.
  This month, thelargest gathering of radiologists and medical physicists takes place in Chicago, hosted by the Radiological Society of North America (RSNA). More than 55,000 will attend.
• 4.
  NVIDIA and itsAI computing platform are driving advancements and breakthroughs across medical imaging with healthcare industry partners. We look at some of them as we head to RSNA.
• 5.
  THE HEADLINER OFRSNA: MACHINE LEARNING Brand new to RSNA this year is the Machine Learning Pavilion, featuring AI experts and state-of-the-art technology. Front and center at the show will be the Deep Learning Institute (DLI) which will: “Give attendees a range of hands-on courses to engage with ML tools, write algorithms and improve their understanding of ML technology.” Source: http://www.rsna.org/News.aspx?id=22957 READ ARTICLE
• 6.
  16BIT.AI WINS RSNAMEDICAL IMAGING CONTEST The largest AI medical imaging competition in the world takes place at RSNA each year. 16bit.ai, whose state of the art machine learning algorithms are powered by GPUs, won the Pediatric Bone Age Challenge this year, and will be honored on November 27th at RSNA conference. Assisting physicians’ diagnostic capability is 16bit.ai’s mission is: “To utilize modern developments in machine intelligence to improve the accuracy, reliability, and speed of medical image interpretation while decreasing cost and barriers to healthcare.” Source: http://www.16bit.ai/ LEARN MORE
• 7.
  HEALTHCARE STARTUPS BOOMING Thenumber of AI and deep learning healthcare startups has grown more than 160 percent in the last five years, analysts estimate. Startup Arterys, exhibiting at RSNA in the Machine Learning Pavilion, taps into cloud computation and deep learning to help physicians to measure blood flow through the heart’s ventricles. It’s a process that usually takes 45 minutes. Arterys does it in 15 seconds. “Deep learning is unleashing ideas so futuristic they seem inspired by science fiction. One paper, for example, explores how deep learning can analyze images to help robots perform minimally invasive surgery.” Source: https://blogs.nvidia.com/blog/2017/09/11/medical-imaging-at-miccai/ READ BLOG
• 8.
  CENTER FOR CLINICALDATA SCIENCE TO USE DEEP LEARNING The Center For Clinical Data Science is pursuing deep learning to help find breakthroughs in medical imaging, where Dr. Keith Dreyer states: “We’ve had CAD for a couple of decades, but deep learning is a much better technology. It will provide much higher sensitivity and specificity than we have today, and radiologists will trust it. Integrating it with clinical practice offers many potential benefits.” Source: https://www.forbes.com/sites/tomdavenport/2017/11/05/revolutionizing-radiology-with-deep-learning-at-partners-healthcare-and-many-others/#4cedf4fd5e13 READ ARTICLE
• 9.
  FROM DATA CENTERLAB TO CLINIC To differentiate themselves from the booming number of healthcare startups, the Center of Clinical Data Science (CCDS) is utilizing the NVIDIA DGX-1, an AI supercomputer, to power their research in medical imaging. The findings are having an immediate impact, where CCDS Executive Director Dr. Mark Michalski states: “As we speak, CCDS is taking our breakthroughs straight from the data science lab into doctors’ clinics.” READ BLOG Source: https://blogs.nvidia.com/blog/2017/09/06/ai-assisted-radiology/
• 10.
  HOW AI COULDSPOT LUNG CANCER SOONER – AND SAVE LIVES Lung cancer is the most common cancer worldwide. It’s also one of the most deadly. More than 80 percent of people with lung cancer die within five years of being diagnosed, and half die within a year. H. Michael Park, co- founder of startup Innovation DX, is working to improve those odds. In December, his St. Louis-based medical analytics company plans to release its first product — a GPU-accelerated AI system that detects lung cancer in its early stages from a simple chest X-ray. “Lung cancer is so deadly today because it’s diagnosed so late. We wanted to see if we could help people survive by detecting it early.” Source: https://blogs.nvidia.com/blog/2017/10/30/detecting-lung-cancer/ READ BLOG
• 11.
  AI HELPS GUIDEDECISIONS IN INTENSIVE CARE After her mother suddenly developed a hot pepper allergy, MIT doctoral student Harini Suresh sparked an interest in medical research. Her latest paper: “Shows how GPU-accelerated deep learning predicts whether patients will need certain ICU treatments. The model uses hourly measurements of vital signs — such as blood pressure, heart rate and glucose levels plus patient information like age and gender, to forecast needed treatments.” Source: https://blogs.nvidia.com/blog/2017/10/02/the-ai-will-icu-now-deep-learning-helps-guide-decisions-in-intensive-care/ READ BLOG
• 12.
  Learn more abouthow deep learning is advancing radiology Visit the NVIDIA booth #8543 Machine Learning Pavilion RSNA 2017