Best Product Entry: A HSDK For Ultrasound Imaging

July 2, 2017 by Adam Fabio 33 Comments

As an entry into this year’s Best Product portion of the Hackaday Prize, [kelu124] is developing a hardware and software development kit for ultrasound imaging.

Ultrasound is one of the primary tools used in modern diagnostic medicine. Head to the doctor with abdominal pain, and you can bet you’ll be seeing the business end of an ultrasound system. While Ultrasound systems have gotten cheaper, they aren’t something everyone has in the home yet. [kelu124] is working to change that by building a hardware and software development kit which can be used to explore ultrasound systems. This isn’t [kleu124’s] first rodeo. HSDK builds upon and simplifies Murgen, his first open source ultrasound, and an entry in the 2016 Hackaday prize. [kelu124’s] goal is to “simplify everything, making it more robust and more user-friendly”.

The system is driven by a Raspberry Pi Zero W. A custom carrier board connects the Pi to the pulser block, which sends out the ultrasonic pings, and the analog front end, which receives the reflected signals. The receiver is called Goblin, and is a custom PCB designed [kelu124] designed himself. It uses a variable gain amplifier to bring reflected ultrasound signals up out of the noise.

A system like this would be a boon both to hackers and medical professionals working in the field. Ultrasonics can do more than just imaging. You can decrease healing time with ultrasonics, or even levitate things!

Using A Thermal Camera To Spot A Broken Wrist

July 1, 2017 by Jack Laidlaw 29 Comments

Chemist and Biochemist [Thunderf00t] has shown us a really interesting video in which you can spot the wrist he broke 10 years ago using a thermal camera.

He was on an exercise bike while filming himself on a high-resolution thermal camera, As his body started to heat up he noticed that one hand was not dumping as much heat as the other. In fact one was dumping very little heat. Being a man of science he knew there must be some explanation for this. He eventually came to the conclusion that during a nasty wrist breaking incident about 10 years ago it must have affected the blood-flow to that hand, Which would go on to produce these type of results on a thermal camera while exercising.

Using thermal camera’s to spot fractures in the extremities is nothing new as it has the benefit of eliminating radiation exposure for patients, But it’s not as detailed as an X-ray or as cool as fluoroscopy and is only useful for bones near the surface of the skin. It’s still great that you can visualize this for yourself and even after 10 years still notice a significant difference.

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Using Machine Learning To Cut Down Surgical Videos

June 26, 2017 by Lewin Day 5 Comments

Recording video of medical surgeries is a great way to both educate doctors in training and identify process improvements. However, surgeries can be very time consuming, and it can be a gargantuan task to sort through endless hours of video searching for relevant points where the action happens. To tackle this issue, researchers at MIT have used machine learning techniques to analyse videos of surgical procedures.

There’s some fairly serious mathematics behind this sort of videographic analysis.

The machine learning algorithm needed to be trained to identify the relevant parts of surgical videos. To do this, the laparoscopic surgeries being investigated were split up into distinct stages, each relating to a different part of the surgical process. Researchers would then watch recordings of prior surgeries and mark the start of each stage. This data was used to train the model which was then used to sift through other recordings to capture the key moments of each surgery.

The time-saving advantages of such technology could be applied to a great many fields – such an algorithm could be put to great use to sort through hours of uneventful security footage looking for anomalies, or rapidly cut together holiday footage so you only have to see the good parts. We’d love to see the researchers release footage showing the algorithm’s work – thus far, all we have to go off is the project paper.

If you’re thirsty for more machine learning knowledge, read up on the state of working with neural networks in 2017.

Listen To Your Body

June 24, 2017 by Al Williams 14 Comments

[John Miller] has the perfect response next time he complains about an ache or pain and one of his friends says, “You should listen to your body!” As you can see in the video below, he already does. Using two 9V batteries and some instrumentation amplifiers, [John] built an electromyography (EMG) rig.

If you haven’t heard of EMG, think of EEG or EKG, but for muscles instead of your brain or your heart. The LT1167 amplifier is well-suited for this application and even has a data sheet showing how to create an EMG circuit. [John] also used some more garden-variety op amps and the ubiquitous LM386N chip for audio amplification.

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Getting Data Off Proprietary Glucometers Gets A Little Easier

June 23, 2017 by Donald Papp 22 Comments

Glucometers (which measure glucose levels in blood) are medical devices familiar to diabetics, and notorious for being proprietary. Gentoo Linux developer [Flameeyes] has some good news about his open source tool to read and export data from a growing variety of glucometers. For [Flameeyes], the process started four years ago when he needed to send his glucometer readings to his doctor and ended up writing his own tool. Previously it was for Linux only, but now has Windows support.

Glucometers use a variety of different data interfaces, and even similar glucometers from the same manufacturer can use different protocols. Getting the data is one thing, but more is needed. [Flameeyes] admits that the tool is still crude in many ways, lacking useful features such as HTML output. Visualization and analysis are missing as well. If you’re interested in seeing if you can help, head over to the GitHub repository for glucomerutils. Also needed are details on protocols used by different devices; [Flameeyes] has only been able to reverse-engineer the protocols of meters he owns.

Speaking of glucometers, there is a project for a Universal Glucometer which aims to be able to use test strips from any manufacturer without needing to purchase a different meter.

Thanks for the tip, [Stuart]!

Go Small, Get Big: The Hack That Revolutionized Bioscience

June 23, 2017 by Dan Maloney 11 Comments

Few people outside the field know just how big bioscience can get. The public tends to think of fields like physics and astronomy, with their huge particle accelerators and massive telescopes, as the natural expressions of big science. But for decades, biology has been getting bigger, especially in the pharmaceutical industry. Specialized labs built around the automation equipment that enables modern pharmaceutical research would dazzle even the most jaded CERN physicist, with fleets of robot arms moving labware around in an attempt to find the Next Big Drug.

I’ve written before on big biology and how to get more visibility for the field into STEM programs. But how exactly did biology get big? What enabled biology to grow beyond a rack of test tubes to the point where experiments with millions of test occasions are not only possible but practically required? Was it advances in robots, or better detection methodologies? Perhaps it was a breakthrough in genetic engineering?

Nope. Believe it or not, it was a small block of plastic with some holes drilled in it. This is the story of how the microtiter plate allowed bioscience experiments to be miniaturized to the point where hundreds or thousands of tests can be done at a time.

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MRIs: Why Are They So Loud?

June 20, 2017 by Dan Maloney 117 Comments

My dad was scheduled for his first MRI scan the other day, and as the designated family technical expert, Pop had plenty of questions for me about what to expect. I told him everything I knew about the process, having had a few myself, but after the exam he asked the first question that everyone seems to ask: “Why is that thing so damn loud?”

Sadly, I didn’t have an answer for him. I’ve asked the same question myself after my MRIs, hoping for a tech with a little more time and lot more interest in the technology he or she uses to answer me with more than the “it’s the machine that makes the noise” brush-off. Well, duh.

MRI is one of those technologies that I don’t feel I have a firm enough grasp on, and it seems like something I should really be better versed in. So I decided to delve into the innards of these modern medical marvels to see if I can answer this basic question, plus see if I can address a few more complicated questions.

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