As part of the effort to explore NVidia Jetson Nano, and part of its AI Specialist course (after finishing Fundamentals of AI in Nvidia Deep Learning Institute), I started buliding a JetBot.

JetBots are well documented and relatively easy to build provdied you have the right parts. There is also a bill of materials to make purchasing simpler.

The chassis was 3D printed according to the full DIY instructions (did not use a kit).

The camera used in this picture is actually from a Rasp Pi infrared camera I bought years ago. Turns out I could remove the lens and apply to another camera I bought for this project (IMX290-160FOV). It turns out that the 70 degree FOV on the lens was really just not wide enough to see what is going on. The 160-degree FOV was perfect and seems to help the bot see around itself.

The details can be downloaded here.

A few snippets…

“Gift funds used in support of the OEA project have a deficit balance of $11.59 million as of August 31, 2016, meaning that MD Anderson spent gift monies it has not yet received from donors.”

“Agreement with PricewaterhouseCoopers (PwC) for “Business Plan for a Flagship Informatics Tool” to lead an assessment of the “capabilities necessary to build the tool” and “incorporate the outcome of the assessment into a business plan that will guide the development” of the tool.”

“The first MD Anderson contract related to development of OEA using Watson technology was signed with IBM in June 2012… The original contract terms were for six months at a fixed fee of $2.4 million. That contract has been extended 12 times, with total fees of $39.2 million. The current extension expired on October 31, 2016.”

Interestingly, if you search in the document for “radiology” or “imaging”:

That said, it is a good cautionary tale for the radiologist-informaticist because the value proposition very closely mirrored what we are hearing in imaging today.

Just swap out the words “treatment,” “clinical-trial,” and “therapy” with “diagnosis” and “imaging”:

Artificial intelligence promises to uproot the practice of medical imaging – traditionally thought to be expensive and highly expert-driven. Radiology industry juggernauts like General Electric, Nuance, and Partners HealthCare all teaming up with established AI players like Intel and NVidia. The innovations have advanced rapidly. Recently, AI has managed to achieve super-human accuracy in the detection of pneumonia on radiography.

More interestingly, some vendors are marketing FDA-approved AI to expedite stroke treatment and eliminating diagnostic delays. The definition of “delay?” Human radiologists taking time to read those head CTs.

So what’s a radiology practice to do?

What can we learn from other industries that have seen the arrival of large, untamable, data- and AI-powered competitors?

Amazon entering an industry is typically regarded as an extinction-level event. Amazon started with the Internet, then had Big Data, and now has AI – they’ve bet first, bet big, and bet right on all of the major tech trends.

But this story isn’t about Amazon; it’s about everybody else.

Continue reading →

In this briefing:

• Research: F-18 FDG PET-CT predicts Alzheimer’s disease with 100% sensitivity and 82% specificity
• Research: An “explainable AI” model for intracranial hemorrhage
• Hardware: Specialized AI hardware promises to accelerate network training over 1000-fold in 5 years.
• Startup: Philips funds 19 AI-related imaging startups in 14 countries
• Marketing: Healthcare AI expected to grow 20-fold by 2024

Stay up to speed in 2 minutes. Radiology AI Briefing is a semi-regular series of blog posts featuring hand-picked news stories and summaries on machine learning and data science.

Continue reading →