The University College London (UCL) New AI-Powered X-Ray technique helps detect explosives and also identify cancer. The latest technology pairs x rays and AI to detect tumors that would be fatal in humans.
The new AI-Powered X-Ray technique is excellent at picking up materials even when they’re hidden inside other objects. With a time efficient AI, you can create relevant blog posts in minutes, rather than hours.
Even if there is “a little bit of texture in the middle of many other things” , the algorithm will find it.
Medical applications for this AI include cancer screening. The researchers believe that this technique could be implemented into these applications in the future.
Although the researchers have not yet tested whether the technique can successfully detect tumors in breast tissue, they are excited about the possibility of detecting very small ones.
One day, physicians may be able to detect tumors by scanning for texture. Dr. Olivo and Professor Rogers are currently assessing their current AI’s ability to find texture in tumors, which is an advance that could lead to earlier diagnosis.
The traditional detection technique of x-rays can be difficult to use when explosives are shielded from exposure by electronics. However, the new AI-Powered X-Ray technique had a 100 percent success rate in test settings.
Bombing materials were hidden inside everyday items in bags to blend with what someone might carry onto an airplane.
Instead of using x-rays, researchers used a specially constructed machine with masks to scan the bags. Though theoretically any mask could be used, it would need to contain an even number of holes in order to split a uniform field into an array of smaller beamlets.
The beamlets had scattering angles as small as a microradian, which means that they were only moving 200 times larger than the size of a degree. When examining the scatter, AI was trained to identify the texture of specific material based on how angle changes.Scientists concluded that even though it would be unrealistic to expect perfect accuracy in large-scale studies, the algorithm was able to always identify explosives under laboratory conditions.
Kevin Wells said that this latest work from the UCL teams looks promising and has a huge potential for the task of threat detection.
Kevin Smith, Head of Operations at Athena Diagnostics, has said that they’ll be interested in seeing how the use of AI to detect cancer evolves.
The research study was first published in the scientific journal Nature Communications on Friday.
As phase-based methods were developed, the detail of contrast images was enhanced and dark-field imaging had better sensitivity to objects at various levels. We found that dark-field creates a texture whose pattern can be characteristic of the material being imaged, and when combined with attenuation leads to improved detection of hazardous materials. We also found that these ambiguities could be resolved through machine learning by computing the texture in dark-field and comparing it with an “average” texture created from all data.
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